CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition
Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Qinghe [verfasserIn] Tian, Xinyu [verfasserIn] Yang, Mingqiang [verfasserIn] Su, Huake [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Multidimensional systems and signal processing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990, 32(2020), 1 vom: 17. Juli, Seite 239-262 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2020 ; number:1 ; day:17 ; month:07 ; pages:239-262 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11045-020-00736-x |
|---|
| Katalog-ID: |
SPR04298825X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR04298825X | ||
| 003 | DE-627 | ||
| 005 | 20220111024946.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210204s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11045-020-00736-x |2 doi | |
| 035 | |a (DE-627)SPR04298825X | ||
| 035 | |a (DE-599)SPRs11045-020-00736-x-e | ||
| 035 | |a (SPR)s11045-020-00736-x-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |q ASE |
| 084 | |a 31.00 |2 bkl | ||
| 100 | 1 | |a Zheng, Qinghe |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. | ||
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Road type recognition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Model pruning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Model compression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Inference speedup |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tian, Xinyu |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Mingqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Su, Huake |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multidimensional systems and signal processing |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 |g 32(2020), 1 vom: 17. Juli, Seite 239-262 |w (DE-627)271178191 |w (DE-600)1479232-1 |x 1573-0824 |7 nnns |
| 773 | 1 | 8 | |g volume:32 |g year:2020 |g number:1 |g day:17 |g month:07 |g pages:239-262 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s11045-020-00736-x |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a SSG-OPC-ASE | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_138 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_152 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_187 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_250 | ||
| 912 | |a GBV_ILN_281 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_636 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2001 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2008 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2010 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2025 | ||
| 912 | |a GBV_ILN_2026 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2031 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2037 | ||
| 912 | |a GBV_ILN_2038 | ||
| 912 | |a GBV_ILN_2039 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2093 | ||
| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2107 | ||
| 912 | |a GBV_ILN_2108 | ||
| 912 | |a GBV_ILN_2110 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2144 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2188 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_2232 | ||
| 912 | |a GBV_ILN_2336 | ||
| 912 | |a GBV_ILN_2446 | ||
| 912 | |a GBV_ILN_2470 | ||
| 912 | |a GBV_ILN_2472 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2522 | ||
| 912 | |a GBV_ILN_2548 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4246 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4328 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4336 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 912 | |a GBV_ILN_4700 | ||
| 936 | b | k | |a 31.00 |q ASE |
| 951 | |a AR | ||
| 952 | |d 32 |j 2020 |e 1 |b 17 |c 07 |h 239-262 | ||
| author_variant |
q z qz x t xt m y my h s hs |
|---|---|
| matchkey_str |
article:15730824:2020----::licosaemnflivracbsdrnnmtoodecnouinlerlewrf |
| hierarchy_sort_str |
2020 |
| bklnumber |
31.00 |
| publishDate |
2020 |
| allfields |
10.1007/s11045-020-00736-x doi (DE-627)SPR04298825X (DE-599)SPRs11045-020-00736-x-e (SPR)s11045-020-00736-x-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Zheng, Qinghe verfasserin aut CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. Deep learning (dpeaa)DE-He213 Road type recognition (dpeaa)DE-He213 Model pruning (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Inference speedup (dpeaa)DE-He213 Tian, Xinyu verfasserin aut Yang, Mingqiang verfasserin aut Su, Huake verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 32(2020), 1 vom: 17. Juli, Seite 239-262 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:32 year:2020 number:1 day:17 month:07 pages:239-262 https://dx.doi.org/10.1007/s11045-020-00736-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 32 2020 1 17 07 239-262 |
| spelling |
10.1007/s11045-020-00736-x doi (DE-627)SPR04298825X (DE-599)SPRs11045-020-00736-x-e (SPR)s11045-020-00736-x-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Zheng, Qinghe verfasserin aut CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. Deep learning (dpeaa)DE-He213 Road type recognition (dpeaa)DE-He213 Model pruning (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Inference speedup (dpeaa)DE-He213 Tian, Xinyu verfasserin aut Yang, Mingqiang verfasserin aut Su, Huake verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 32(2020), 1 vom: 17. Juli, Seite 239-262 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:32 year:2020 number:1 day:17 month:07 pages:239-262 https://dx.doi.org/10.1007/s11045-020-00736-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 32 2020 1 17 07 239-262 |
| allfields_unstemmed |
10.1007/s11045-020-00736-x doi (DE-627)SPR04298825X (DE-599)SPRs11045-020-00736-x-e (SPR)s11045-020-00736-x-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Zheng, Qinghe verfasserin aut CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. Deep learning (dpeaa)DE-He213 Road type recognition (dpeaa)DE-He213 Model pruning (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Inference speedup (dpeaa)DE-He213 Tian, Xinyu verfasserin aut Yang, Mingqiang verfasserin aut Su, Huake verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 32(2020), 1 vom: 17. Juli, Seite 239-262 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:32 year:2020 number:1 day:17 month:07 pages:239-262 https://dx.doi.org/10.1007/s11045-020-00736-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 32 2020 1 17 07 239-262 |
| allfieldsGer |
10.1007/s11045-020-00736-x doi (DE-627)SPR04298825X (DE-599)SPRs11045-020-00736-x-e (SPR)s11045-020-00736-x-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Zheng, Qinghe verfasserin aut CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. Deep learning (dpeaa)DE-He213 Road type recognition (dpeaa)DE-He213 Model pruning (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Inference speedup (dpeaa)DE-He213 Tian, Xinyu verfasserin aut Yang, Mingqiang verfasserin aut Su, Huake verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 32(2020), 1 vom: 17. Juli, Seite 239-262 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:32 year:2020 number:1 day:17 month:07 pages:239-262 https://dx.doi.org/10.1007/s11045-020-00736-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 32 2020 1 17 07 239-262 |
| allfieldsSound |
10.1007/s11045-020-00736-x doi (DE-627)SPR04298825X (DE-599)SPRs11045-020-00736-x-e (SPR)s11045-020-00736-x-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Zheng, Qinghe verfasserin aut CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. Deep learning (dpeaa)DE-He213 Road type recognition (dpeaa)DE-He213 Model pruning (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Inference speedup (dpeaa)DE-He213 Tian, Xinyu verfasserin aut Yang, Mingqiang verfasserin aut Su, Huake verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 32(2020), 1 vom: 17. Juli, Seite 239-262 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:32 year:2020 number:1 day:17 month:07 pages:239-262 https://dx.doi.org/10.1007/s11045-020-00736-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 32 2020 1 17 07 239-262 |
| language |
English |
| source |
Enthalten in Multidimensional systems and signal processing 32(2020), 1 vom: 17. Juli, Seite 239-262 volume:32 year:2020 number:1 day:17 month:07 pages:239-262 |
| sourceStr |
Enthalten in Multidimensional systems and signal processing 32(2020), 1 vom: 17. Juli, Seite 239-262 volume:32 year:2020 number:1 day:17 month:07 pages:239-262 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Deep learning Road type recognition Model pruning Model compression Inference speedup |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Multidimensional systems and signal processing |
| authorswithroles_txt_mv |
Zheng, Qinghe @@aut@@ Tian, Xinyu @@aut@@ Yang, Mingqiang @@aut@@ Su, Huake @@aut@@ |
| publishDateDaySort_date |
2020-07-17T00:00:00Z |
| hierarchy_top_id |
271178191 |
| dewey-sort |
3510 |
| id |
SPR04298825X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR04298825X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024946.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210204s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11045-020-00736-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR04298825X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs11045-020-00736-x-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11045-020-00736-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Qinghe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Road type recognition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model pruning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model compression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inference speedup</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Xinyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Mingqiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Su, Huake</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multidimensional systems and signal processing</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990</subfield><subfield code="g">32(2020), 1 vom: 17. Juli, Seite 239-262</subfield><subfield code="w">(DE-627)271178191</subfield><subfield code="w">(DE-600)1479232-1</subfield><subfield code="x">1573-0824</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:239-262</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11045-020-00736-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ASE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.00</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">07</subfield><subfield code="h">239-262</subfield></datafield></record></collection>
|
| author |
Zheng, Qinghe |
| spellingShingle |
Zheng, Qinghe ddc 510 bkl 31.00 misc Deep learning misc Road type recognition misc Model pruning misc Model compression misc Inference speedup CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition |
| authorStr |
Zheng, Qinghe |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)271178191 |
| format |
electronic Article |
| dewey-ones |
510 - Mathematics |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1573-0824 |
| topic_title |
510 ASE 31.00 bkl CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition Deep learning (dpeaa)DE-He213 Road type recognition (dpeaa)DE-He213 Model pruning (dpeaa)DE-He213 Model compression (dpeaa)DE-He213 Inference speedup (dpeaa)DE-He213 |
| topic |
ddc 510 bkl 31.00 misc Deep learning misc Road type recognition misc Model pruning misc Model compression misc Inference speedup |
| topic_unstemmed |
ddc 510 bkl 31.00 misc Deep learning misc Road type recognition misc Model pruning misc Model compression misc Inference speedup |
| topic_browse |
ddc 510 bkl 31.00 misc Deep learning misc Road type recognition misc Model pruning misc Model compression misc Inference speedup |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Multidimensional systems and signal processing |
| hierarchy_parent_id |
271178191 |
| dewey-tens |
510 - Mathematics |
| hierarchy_top_title |
Multidimensional systems and signal processing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)271178191 (DE-600)1479232-1 |
| title |
CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition |
| ctrlnum |
(DE-627)SPR04298825X (DE-599)SPRs11045-020-00736-x-e (SPR)s11045-020-00736-x-e |
| title_full |
CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition |
| author_sort |
Zheng, Qinghe |
| journal |
Multidimensional systems and signal processing |
| journalStr |
Multidimensional systems and signal processing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2020 |
| contenttype_str_mv |
txt |
| container_start_page |
239 |
| author_browse |
Zheng, Qinghe Tian, Xinyu Yang, Mingqiang Su, Huake |
| container_volume |
32 |
| class |
510 ASE 31.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Zheng, Qinghe |
| doi_str_mv |
10.1007/s11045-020-00736-x |
| dewey-full |
510 |
| author2-role |
verfasserin |
| title_sort |
clmip: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition |
| title_auth |
CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition |
| abstract |
Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. |
| abstractGer |
Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. |
| abstract_unstemmed |
Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 |
| container_issue |
1 |
| title_short |
CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition |
| url |
https://dx.doi.org/10.1007/s11045-020-00736-x |
| remote_bool |
true |
| author2 |
Tian, Xinyu Yang, Mingqiang Su, Huake |
| author2Str |
Tian, Xinyu Yang, Mingqiang Su, Huake |
| ppnlink |
271178191 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11045-020-00736-x |
| up_date |
2024-07-03T16:00:21.202Z |
| _version_ |
1803574230342696960 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR04298825X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024946.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210204s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11045-020-00736-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR04298825X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs11045-020-00736-x-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11045-020-00736-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Qinghe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recently, deep learning based models have demonstrated the superiority in a variety of visual tasks like object detection and instance segmentation. In practical applications, deploying advanced networks into real-time applications such as autonomous driving is still challenging due to expensive computational cost and memory footprint. In this paper, to reduce the size of deep convolutional neural network (CNN) and accelerate its reasoning, we propose a cross-layer manifold invariance based pruning method named CLMIP for network compression to help it complete real-time road type recognition in low-cost vision system. Manifolds are higher-dimensional analogues of curves and surfaces, which can be self-organized to reflect the data distribution and characterize the relationship between data. Therefore, we hope to guarantee the generalization ability of deep CNN by maintaining the consistency of the data manifolds of each layer in the network, and then remove the parameters with less influence on the manifold structure. Therefore, CLMIP can be regarded as a tool to further investigate the dependence of model structure on network optimization and generalization. To the best of our knowledge, this is the first time to prune deep CNN based on the invariance of data manifolds. During experimental process, we use the python based keyword crawler program to collect 102 first-view videos of car cameras, including 137 200 images (320 × 240) of four road scenes (urban road, off-road, trunk road and motorway). Finally, the classification results have demonstrated that CLMIP can achieve state-of-the-art performance with a speed of 26 FPS on NVIDIA Jetson Nano.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Road type recognition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model pruning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model compression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inference speedup</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Xinyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Mingqiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Su, Huake</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multidimensional systems and signal processing</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990</subfield><subfield code="g">32(2020), 1 vom: 17. Juli, Seite 239-262</subfield><subfield code="w">(DE-627)271178191</subfield><subfield code="w">(DE-600)1479232-1</subfield><subfield code="x">1573-0824</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:239-262</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11045-020-00736-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ASE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.00</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">07</subfield><subfield code="h">239-262</subfield></datafield></record></collection>
|
| score |
7.399884 |