Data reweighting net for web fine-grained image classification

Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often include...
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Autor*in:	Liu, Yifeng [verfasserIn] Wu, Zhenxin [verfasserIn] Lo, Sio-long [verfasserIn] Chen, Zhenqiang [verfasserIn] Ke, Gang [verfasserIn] Yue, Chuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Web images Fine-grained visual classification Noisy labels Data Reweighting

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Springer US, 1995, 83(2024), 33 vom: 02. März, Seite 79985-80005
Übergeordnetes Werk:	volume:83 ; year:2024 ; number:33 ; day:02 ; month:03 ; pages:79985-80005

Links:	Volltext

DOI / URN:	10.1007/s11042-024-18598-x

Katalog-ID:	SPR057698821

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allfields	10.1007/s11042-024-18598-x doi (DE-627)SPR057698821 (SPR)s11042-024-18598-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Liu, Yifeng verfasserin aut Data reweighting net for web fine-grained image classification 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. Web images (dpeaa)DE-He213 Fine-grained visual classification (dpeaa)DE-He213 Noisy labels (dpeaa)DE-He213 Data Reweighting (dpeaa)DE-He213 Wu, Zhenxin verfasserin aut Lo, Sio-long verfasserin (orcid)0000-0002-5296-0922 aut Chen, Zhenqiang verfasserin aut Ke, Gang verfasserin aut Yue, Chuan verfasserin aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2024), 33 vom: 02. März, Seite 79985-80005 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2024 number:33 day:02 month:03 pages:79985-80005 https://dx.doi.org/10.1007/s11042-024-18598-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2119 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_2574 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 54.87 VZ AR 83 2024 33 02 03 79985-80005
spelling	10.1007/s11042-024-18598-x doi (DE-627)SPR057698821 (SPR)s11042-024-18598-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Liu, Yifeng verfasserin aut Data reweighting net for web fine-grained image classification 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. Web images (dpeaa)DE-He213 Fine-grained visual classification (dpeaa)DE-He213 Noisy labels (dpeaa)DE-He213 Data Reweighting (dpeaa)DE-He213 Wu, Zhenxin verfasserin aut Lo, Sio-long verfasserin (orcid)0000-0002-5296-0922 aut Chen, Zhenqiang verfasserin aut Ke, Gang verfasserin aut Yue, Chuan verfasserin aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2024), 33 vom: 02. März, Seite 79985-80005 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2024 number:33 day:02 month:03 pages:79985-80005 https://dx.doi.org/10.1007/s11042-024-18598-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2119 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_2574 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 54.87 VZ AR 83 2024 33 02 03 79985-80005
allfields_unstemmed	10.1007/s11042-024-18598-x doi (DE-627)SPR057698821 (SPR)s11042-024-18598-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Liu, Yifeng verfasserin aut Data reweighting net for web fine-grained image classification 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. Web images (dpeaa)DE-He213 Fine-grained visual classification (dpeaa)DE-He213 Noisy labels (dpeaa)DE-He213 Data Reweighting (dpeaa)DE-He213 Wu, Zhenxin verfasserin aut Lo, Sio-long verfasserin (orcid)0000-0002-5296-0922 aut Chen, Zhenqiang verfasserin aut Ke, Gang verfasserin aut Yue, Chuan verfasserin aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2024), 33 vom: 02. März, Seite 79985-80005 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2024 number:33 day:02 month:03 pages:79985-80005 https://dx.doi.org/10.1007/s11042-024-18598-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2119 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_2574 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 54.87 VZ AR 83 2024 33 02 03 79985-80005
allfieldsGer	10.1007/s11042-024-18598-x doi (DE-627)SPR057698821 (SPR)s11042-024-18598-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Liu, Yifeng verfasserin aut Data reweighting net for web fine-grained image classification 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. Web images (dpeaa)DE-He213 Fine-grained visual classification (dpeaa)DE-He213 Noisy labels (dpeaa)DE-He213 Data Reweighting (dpeaa)DE-He213 Wu, Zhenxin verfasserin aut Lo, Sio-long verfasserin (orcid)0000-0002-5296-0922 aut Chen, Zhenqiang verfasserin aut Ke, Gang verfasserin aut Yue, Chuan verfasserin aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2024), 33 vom: 02. März, Seite 79985-80005 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2024 number:33 day:02 month:03 pages:79985-80005 https://dx.doi.org/10.1007/s11042-024-18598-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2119 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_2574 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 54.87 VZ AR 83 2024 33 02 03 79985-80005
allfieldsSound	10.1007/s11042-024-18598-x doi (DE-627)SPR057698821 (SPR)s11042-024-18598-x-e DE-627 ger DE-627 rakwb eng 070 004 VZ 54.87 bkl Liu, Yifeng verfasserin aut Data reweighting net for web fine-grained image classification 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. Web images (dpeaa)DE-He213 Fine-grained visual classification (dpeaa)DE-He213 Noisy labels (dpeaa)DE-He213 Data Reweighting (dpeaa)DE-He213 Wu, Zhenxin verfasserin aut Lo, Sio-long verfasserin (orcid)0000-0002-5296-0922 aut Chen, Zhenqiang verfasserin aut Ke, Gang verfasserin aut Yue, Chuan verfasserin aut Enthalten in Multimedia tools and applications Springer US, 1995 83(2024), 33 vom: 02. März, Seite 79985-80005 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2024 number:33 day:02 month:03 pages:79985-80005 https://dx.doi.org/10.1007/s11042-024-18598-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI 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_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2119 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_2574 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 54.87 VZ AR 83 2024 33 02 03 79985-80005
language	English
source	Enthalten in Multimedia tools and applications 83(2024), 33 vom: 02. März, Seite 79985-80005 volume:83 year:2024 number:33 day:02 month:03 pages:79985-80005
sourceStr	Enthalten in Multimedia tools and applications 83(2024), 33 vom: 02. März, Seite 79985-80005 volume:83 year:2024 number:33 day:02 month:03 pages:79985-80005
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Web images Fine-grained visual classification Noisy labels Data Reweighting
dewey-raw	070
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Liu, Yifeng @@aut@@ Wu, Zhenxin @@aut@@ Lo, Sio-long @@aut@@ Chen, Zhenqiang @@aut@@ Ke, Gang @@aut@@ Yue, Chuan @@aut@@
publishDateDaySort_date	2024-03-02T00:00:00Z
hierarchy_top_id	27135030X
dewey-sort	270
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language_de	englisch
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author	Liu, Yifeng
spellingShingle	Liu, Yifeng ddc 070 bkl 54.87 misc Web images misc Fine-grained visual classification misc Noisy labels misc Data Reweighting Data reweighting net for web fine-grained image classification
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title	Data reweighting net for web fine-grained image classification
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title_sort	data reweighting net for web fine-grained image classification
title_auth	Data reweighting net for web fine-grained image classification
abstract	Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Fine-grained visual classification (FGVC) necessitates expert knowledge,which is expensive and requires a large training sample size. Consequently, using sample data acquired through the web has emerged as a novel approach for augmenting training samples. However, the web data often includes noisy samples, leading to misclassification of deep learning models. This paper presents a a meta-learning-base method called Data Reweighting Net (DR-Net). It enables the use of small, clean meta set as a guiding mechanism to accurately learn web image datasets that contain noise. More specifically, the DR-Net fully learns from small, clean meta set to discard noisy samples and obtain clean web samples through low similarity properties. DR-Net enables classification networks to adaptively learn training sets through sample weighting, mitigating the impact of noisy labels on classification learning. Our experiments on Web-bird, Web-aircraft, Web-car, CIFAR-10, and CIFAR-100 datasets demonstrate the feasibility of our proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Data reweighting net for web fine-grained image classification
url	https://dx.doi.org/10.1007/s11042-024-18598-x
remote_bool	true
author2	Wu, Zhenxin Lo, Sio-long Chen, Zhenqiang Ke, Gang Yue, Chuan
author2Str	Wu, Zhenxin Lo, Sio-long Chen, Zhenqiang Ke, Gang Yue, Chuan
ppnlink	27135030X
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doi_str	10.1007/s11042-024-18598-x
up_date	2024-10-08T05:15:04.266Z
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