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We present a simple method, CropMix, for the purpose of producing a rich input distribution from the original dataset distribution.
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X. Chen and K. He · 2021
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Deep anomaly detection with outlier exposure
D. Hendrycks, M. Mazeika, and T. Dietterich · 2018
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Data augmentation by pairing samples for images classification
H. Inoue · 2018
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Augmix: A simple data processing method to improve robustness and uncertainty
D. Hendrycks, N. Mu, E. D. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan · 2019
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Fixing the train-test resolution discrepancy
H. Touvron, A. Vedaldi, M. Douze, and H. Jégou · 2019
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X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, and J. Sun · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song · 2021
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D. Hendrycks, A. Zou, M. Mazeika, L. Tang, D. Song, and J. Steinhardt · 2021
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J.-H. Kim, W. Choo, H. Jeong, and H. O. Song · 2021
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Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo · 2021
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P. Fang, X. Li, Y. Yan, S. Zhang, Q. Kang, X. Li, and Z. L. and · 2022
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You only cut once: Boosting data augmentation with a single cut
J. Han, P. Fang, W. Li, J. Hong, M. A. Armin, , I. Reid, L. Petersson, and H. Li · 2022
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Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick · 2022
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Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
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Crafting better contrastive views for siamese representation learning
X. Peng, K. Wang, Z. Zhu, and Y. You · 2022
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H. Touvron, M. Cord, and H. Jégou · 2022
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On the importance of asymmetry for siamese representation learning
X. Wang, H. Fan, Y. Tian, D. Kihara, and X. Chen · 2022
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