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We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Best practices for convolutional neural networks applied to visual document analysis
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Extraction de séquences numériques dans des documents manuscrits quelconques
Clément Chatelain · 2006
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Automated flower classification over a large number of classes
M-E. Nilsback and A. Zisserman · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Deep big multilayer perceptrons for digit recognition
Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella, and Jürgen Schmidhuber · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, and Thomas Brox · 2014
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Deep neural networks for large vocabulary handwritten text recognition
Théodore Bluche · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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How far can we go without convolution: Improving fully-connected networks
Zhouhan Lin, Roland Memisevic, and Kishore Konda · 2015
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Tensorizing neural networks
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, and Dmitry P Vetrov · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (GELUs)
Dan Hendrycks and Kevin Gimpel · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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The iNaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Alexander Shepard, Hartwig Adam, Pietro Perona, and Serge J. Belongie · 2017
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2017
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Do deep convolutional nets really need to be deep and convolutional?
Gregor Urban, Krzysztof J Geras, Samira Ebrahimi Kahou, Ozlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, and Matt Richardson · 2017
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CutMix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Lucas Beyer, Olivier J. Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aaron van den Oord · 2020
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Augment your batch: Improving generalization through instance repetition
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2020
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Towards learning convolutions from scratch
Behnam Neyshabur · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2020
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2017
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The best of both worlds: Combining recent advances in neural machine translation
Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Niki Parmar, Mike Schuster, Zhifeng Chen, et al · 2018
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How many samples are needed to estimate a convolutional neural network?
Simon S Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, and Aarti Singh · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
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Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli · 2018
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Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, and Che Zheng · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, M. Cord, M. Douze, F. Massa, Alexandre Sablayrolles, and H. Jégou · 2020
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Grafit: Learning fine-grained image representations with coarse labels
Hugo Touvron, Alexandre Sablayrolles, M. Douze, M. Cord, and H. Jégou · 2020
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Large batch optimization for deep learning: Training BERT in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2020
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Exploring self-attention for image recognition
Hengshuang Zhao, Jiaya Jia, and Vladlen Koltun · 2020
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
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Revisiting ResNets: Improved training and scaling strategies
Irwan Bello, W. Fedus, Xianzhi Du, E. D. Cubuk, A. Srinivas, Tsung-Yi Lin, Jonathon Shlens, and Barret Zoph · 2021
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High-performance large-scale image recognition without normalization
A. Brock, Soham De, S. L. Smith, and K. Simonyan · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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RepMLP: Re-parameterizing convolutions into fully-connected layers for image recognition
Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Differentiable model compression via pseudo quantization noise
Alexandre Défossez, Yossi Adi, and Gabriel Synnaeve · 2021
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Training with quantization noise for extreme model compression
Angela Fan, Pierre Stock, Benjamin Graham, Edouard Grave, Rémi Gribonval, Hervé Jégou, and Armand Joulin · 2021
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Hanxiao Liu, Zihang Dai, David R. So, and Quoc V. Le · 2021
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Do you even need attention? A stack of feed-forward layers does surprisingly well on ImageNet
Luke Melas-Kyriazi · 2021
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MLP-Mixer: An all-MLP architecture for vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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