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In the realm of visual recognition, data augmentation stands out as a pivotal technique to amplify model robustness.
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Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, and Christopher Ré, · 2019
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Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan, · 2019
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Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le, · 2019
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“A unified game-theoretic interpretation of adversarial robustness,”
Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, et al., · 2021
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“On interaction between augmentations and corruptions in natural corruption robustness,”
Eric Mintun, Alexander Kirillov, and Saining Xie, · 2021
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“Discovering and explaining the representation bottleneck of dnns,”
Huiqi Deng, Qihan Ren, Xu Chen, Hao Zhang, Jie Ren, and Quanshi Zhang, · 2021
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Xu Cheng, Chuntung Chu, Yi Zheng, Jie Ren, and Quanshi Zhang, · 2021
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“Trivialaugment: Tuning-free yet state-of-the-art data augmentation,”
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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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“A group-theoretic framework for data augmentation,”
Shuxiao Chen, Edgar Dobriban, and Jane H Lee, · 2020
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Hao Zhang, Sen Li, Yinchao Ma, Mingjie Li, Yichen Xie, and Quanshi Zhang, · 2020
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“The many faces of robustness: A critical analysis of out-of-distribution generalization,”
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al., · 2021
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Samuel G. Müller and Frank Hutter, · 2021
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“Enhance the visual representation via discrete adversarial training,”
Xiaofeng Mao, Yuefeng Chen, Ranjie Duan, Yao Zhu, Gege Qi, Shaokai Ye, Xiaodan Li, Rong Zhang, and Hui Xue, · 2022
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Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, and Jacob Steinhardt, · 2022
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Shuo Yang, Yijun Dong, Rachel Ward, Inderjit S Dhillon, Sujay Sanghavi, and Qi Lei, · 2022
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