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The recent advances in image transformers have shown impressive results and have largely closed the gap between traditional CNN architectures.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 1905
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Randaugment: Practical data augmentation with no separate search
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 1909
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
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, Jakob Uszkoreit, and Neil Houlsby · 2010
Earlier work this paper cites.
Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2010
Earlier work this paper cites.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2012
Earlier work this paper cites.
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 S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2014
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Ya Le and Xuan S. Yang · 2015
Cited alongside, same era.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2017
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le · 2018
Vision transformer for small-size datasets
Seung Hoon Lee, Seunghyun Lee, and Byung Cheol Song · 2021
Later among the works it cites.
Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
Later among the works it cites.
Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
Later among the works it cites.
Ching-Hsun Tseng, Liu-Hsueh Cheng, Shin-Jye Lee, and Xiaojun Zeng · 2021
Later among the works it cites.
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Cited alongside, same era.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
ASAM: adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
Jungmin Kwon, Jeongseop Kim, Hyunseo Park, and In Kwon Choi · 2021
Cited alongside, same era.
Swin transformer V2: scaling up capacity and resolution
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, and Baining Guo
Cited in the paper.
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
Cited in the paper.
Hugo Touvron, Matthieu Cord, and Herve Jegou · 2022
Closest in time.
Minivit: Compressing vision transformers with weight multiplexing
Jinnian Zhang, Houwen Peng, Kan Wu, Mengchen Liu, Bin Xiao, Jianlong Fu, and Lu Yuan · 2022
Closest in time.