Transformers Meet Small Datasets

The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we fi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ran Shao [verfasserIn] Xiao-Jun Bi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Convolutional neural networks small datasets transformer vision transformer

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 118454-118464
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:118454-118464

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3221138

Katalog-ID:	DOAJ086203762

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ086203762
003	DE-627
005	20230502235737.0
007	cr uuu---uuuuu
008	230311s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2022.3221138 \|2 doi
035			\|a (DE-627)DOAJ086203762
035			\|a (DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Ran Shao \|e verfasserin \|4 aut
245	1	0	\|a Transformers Meet Small Datasets
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets.
650		4	\|a Convolutional neural networks
650		4	\|a small datasets
650		4	\|a transformer
650		4	\|a vision transformer
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Xiao-Jun Bi \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 10(2022), Seite 118454-118464 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:10 \|g year:2022 \|g pages:118454-118464
856	4	0	\|u https://doi.org/10.1109/ACCESS.2022.3221138 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2 \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/9944625/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
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_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_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
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_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 10 \|j 2022 \|h 118454-118464

Indexfelder

author_variant	r s rs x j b xjb
matchkey_str	article:21693536:2022----::rnfresetml
hierarchy_sort_str	2022
callnumber-subject-code	TK
publishDate	2022
allfields	10.1109/ACCESS.2022.3221138 doi (DE-627)DOAJ086203762 (DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Shao verfasserin aut Transformers Meet Small Datasets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets. Convolutional neural networks small datasets transformer vision transformer Electrical engineering. Electronics. Nuclear engineering Xiao-Jun Bi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 118454-118464 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:118454-118464 https://doi.org/10.1109/ACCESS.2022.3221138 kostenfrei https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2 kostenfrei https://ieeexplore.ieee.org/document/9944625/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 118454-118464
spelling	10.1109/ACCESS.2022.3221138 doi (DE-627)DOAJ086203762 (DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Shao verfasserin aut Transformers Meet Small Datasets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets. Convolutional neural networks small datasets transformer vision transformer Electrical engineering. Electronics. Nuclear engineering Xiao-Jun Bi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 118454-118464 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:118454-118464 https://doi.org/10.1109/ACCESS.2022.3221138 kostenfrei https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2 kostenfrei https://ieeexplore.ieee.org/document/9944625/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 118454-118464
allfields_unstemmed	10.1109/ACCESS.2022.3221138 doi (DE-627)DOAJ086203762 (DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Shao verfasserin aut Transformers Meet Small Datasets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets. Convolutional neural networks small datasets transformer vision transformer Electrical engineering. Electronics. Nuclear engineering Xiao-Jun Bi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 118454-118464 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:118454-118464 https://doi.org/10.1109/ACCESS.2022.3221138 kostenfrei https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2 kostenfrei https://ieeexplore.ieee.org/document/9944625/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 118454-118464
allfieldsGer	10.1109/ACCESS.2022.3221138 doi (DE-627)DOAJ086203762 (DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Shao verfasserin aut Transformers Meet Small Datasets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets. Convolutional neural networks small datasets transformer vision transformer Electrical engineering. Electronics. Nuclear engineering Xiao-Jun Bi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 118454-118464 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:118454-118464 https://doi.org/10.1109/ACCESS.2022.3221138 kostenfrei https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2 kostenfrei https://ieeexplore.ieee.org/document/9944625/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 118454-118464
allfieldsSound	10.1109/ACCESS.2022.3221138 doi (DE-627)DOAJ086203762 (DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Shao verfasserin aut Transformers Meet Small Datasets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets. Convolutional neural networks small datasets transformer vision transformer Electrical engineering. Electronics. Nuclear engineering Xiao-Jun Bi verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 118454-118464 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:118454-118464 https://doi.org/10.1109/ACCESS.2022.3221138 kostenfrei https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2 kostenfrei https://ieeexplore.ieee.org/document/9944625/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 118454-118464
language	English
source	In IEEE Access 10(2022), Seite 118454-118464 volume:10 year:2022 pages:118454-118464
sourceStr	In IEEE Access 10(2022), Seite 118454-118464 volume:10 year:2022 pages:118454-118464
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Convolutional neural networks small datasets transformer vision transformer Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Ran Shao @@aut@@ Xiao-Jun Bi @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ086203762
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">DOAJ086203762</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502235737.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2022.3221138</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ086203762</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2</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="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ran Shao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Transformers Meet Small Datasets</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convolutional neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">small datasets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">transformer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">vision transformer</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiao-Jun Bi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">10(2022), Seite 118454-118464</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:118454-118464</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2022.3221138</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9944625/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</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_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_65</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_95</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_151</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_213</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_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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_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_4249</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_4335</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="h">118454-118464</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Ran Shao
spellingShingle	Ran Shao misc TK1-9971 misc Convolutional neural networks misc small datasets misc transformer misc vision transformer misc Electrical engineering. Electronics. Nuclear engineering Transformers Meet Small Datasets
authorStr	Ran Shao
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 Transformers Meet Small Datasets Convolutional neural networks small datasets transformer vision transformer
topic	misc TK1-9971 misc Convolutional neural networks misc small datasets misc transformer misc vision transformer misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Convolutional neural networks misc small datasets misc transformer misc vision transformer misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Convolutional neural networks misc small datasets misc transformer misc vision transformer misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	Transformers Meet Small Datasets
ctrlnum	(DE-627)DOAJ086203762 (DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2
title_full	Transformers Meet Small Datasets
author_sort	Ran Shao
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
container_start_page	118454
author_browse	Ran Shao Xiao-Jun Bi
container_volume	10
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Ran Shao
doi_str_mv	10.1109/ACCESS.2022.3221138
author2-role	verfasserin
title_sort	transformers meet small datasets
callnumber	TK1-9971
title_auth	Transformers Meet Small Datasets
abstract	The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets.
abstractGer	The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets.
abstract_unstemmed	The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Transformers Meet Small Datasets
url	https://doi.org/10.1109/ACCESS.2022.3221138 https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2 https://ieeexplore.ieee.org/document/9944625/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Xiao-Jun Bi
author2Str	Xiao-Jun Bi
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2022.3221138
callnumber-a	TK1-9971
up_date	2024-07-03T19:17:29.673Z
_version_	1803586633464807425
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">DOAJ086203762</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502235737.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2022.3221138</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ086203762</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd3e57a62f0cf4470a53774a0c2b282b2</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="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ran Shao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Transformers Meet Small Datasets</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer’s two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convolutional neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">small datasets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">transformer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">vision transformer</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiao-Jun Bi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">10(2022), Seite 118454-118464</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:118454-118464</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2022.3221138</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/d3e57a62f0cf4470a53774a0c2b282b2</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9944625/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</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_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_65</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_95</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_151</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_213</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_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_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</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_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_4249</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_4335</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_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2022</subfield><subfield code="h">118454-118464</subfield></datafield></record></collection>
score	7.401165

Nicht das Richtige dabei?

Schreiben Sie uns!

Transformers Meet Small Datasets

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?