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Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are typically over-parameterized.
Simplifying neural nets by discovering flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1995
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PAC-Bayesian model averaging
David A McAllester · 1999
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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CIFAR-10 and CIFAR-100 datasets
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Gintare Karolina Dziugaite and Daniel M Roy · 2017
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Deep pyramidal residual networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Jorge Nocedal, Ping Tak Peter Tang, Dheevatsa Mudigere, and Mikhail Smelyanskiy · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2018
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2019
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Fisher–Rao metric, geometry, and complexity of neural networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, and James Stokes · 2019
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Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2019
Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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When vision transformers outperform resnets without pre-training or strong data augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong · 2021
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Efficient sharpness-aware minimization for improved training of neural networks
Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, Rick Siow Mong Goh, and Vincent Tan · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Mlp-mixer: An all-mlp architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
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Pytorch image models
Ross Wightman · 2019
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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 · 2020
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2020
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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On the loss landscape of adversarial training: Identifying challenges and how to overcome them
Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, and Sabine Süsstrunk · 2020
Cited alongside, same era.
Normalized flat minima: Exploring scale invariant definition of flat minima for neural networks using pac-bayesian analysis
Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Regularizing neural networks via adversarial model perturbation
Yaowei Zheng, Richong Zhang, and Yongyi Mao · 2021
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DeepViT: Towards deeper vision transformer
Daquan Zhou, Bingyi Kang, Xiaojie Jin, Linjie Yang, Xiaochen Lian, Zihang Jiang, Qibin Hou, and Jiashi Feng · 2021
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Surrogate gap minimization improves sharpness-aware training
Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, James s Duncan, Ting Liu, et al · 2021
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Towards efficient and scalable sharpness-aware minimization
Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You · 2022
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Understanding the robustness in vision transformers
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Animashree Anandkumar, Jiashi Feng, and Jose M Alvarez · 2022
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