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|a 10.1109/TPAMI.2022.3206148
|2 doi
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|a pubmed24n1153.xml
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|a (DE-627)NLM346119324
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|a (NLM)36094972
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Touvron, Hugo
|e verfasserin
|4 aut
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|a ResMLP
|b Feedforward Networks for Image Classification With Data-Efficient Training
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 10.04.2023
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|a Date Revised 10.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently and identically across channels, and (ii) a two-layer feed-forward network in which channels interact independently per patch. When trained with a modern training strategy using heavy data-augmentation and optionally distillation, it attains surprisingly good accuracy/complexity trade-offs on ImageNet. We also train ResMLP models in a self-supervised setup, to further remove priors from employing a labelled dataset. Finally, by adapting our model to machine translation we achieve surprisingly good results. We share pre-trained models and our code based on the Timm library
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|a Journal Article
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|a Bojanowski, Piotr
|e verfasserin
|4 aut
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|a Caron, Mathilde
|e verfasserin
|4 aut
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|a Cord, Matthieu
|e verfasserin
|4 aut
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|a El-Nouby, Alaaeldin
|e verfasserin
|4 aut
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|a Grave, Edouard
|e verfasserin
|4 aut
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|a Izacard, Gautier
|e verfasserin
|4 aut
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|a Joulin, Armand
|e verfasserin
|4 aut
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|a Synnaeve, Gabriel
|e verfasserin
|4 aut
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|a Verbeek, Jakob
|e verfasserin
|4 aut
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|a Jegou, Herve
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 4 vom: 12. Apr., Seite 5314-5321
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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|g volume:45
|g year:2023
|g number:4
|g day:12
|g month:04
|g pages:5314-5321
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|u http://dx.doi.org/10.1109/TPAMI.2022.3206148
|3 Volltext
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