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Data-Augmentation (DA) is known to improve performance across tasks and datasets.
The limiting distributions of certain statistics
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Equation of state calculations by fast computing machines
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Vicinal risk minimization
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Image analysis by tchebichef moments
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The manifold tangent classifier
Rifai, S., Dauphin, Y. N., Vincent, P., Bengio, Y., and Muller, X · 2011
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Understanding dropout
Baldi, P. and Sadowski, P. J · 2013
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In search of the real inductive bias: On the role of implicit regularization in deep learning
Neyshabur, B., Tomioka, R., and Srebro, N · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Bouthillier, X., Konda, K., Vincent, P., and Memisevic, R · 2015
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Spatial transformer networks
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Taqi, A. M., Awad, A., Al-Azzo, F., and Milanova, M · 2018
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A max-affine spline perspective of recurrent neural networks
Wang, Z., Balestriero, R., and Baraniuk, R · 2018
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Three mechanisms of weight decay regularization
Zhang, G., Wang, C., Xu, B., and Grosse, R · 2018
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
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Implicit rugosity regularization via data augmentation
LeJeune, D., Balestriero, R., Javadi, H., and Baraniuk, R. G · 2019
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Jaderberg, M., Simonyan, K., Zisserman, A., et al · 2015
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Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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DeVries, T. and Taylor, G. W · 2017
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Neyshabur, B · 2017
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The effectiveness of data augmentation in image classification using deep learning
Perez, L. and Wang, J · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
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A survey on image data augmentation for deep learning
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A representer theorem for deep neural networks
Unser, M · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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Self-supervised learning of pretext-invariant representations
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Implicit regularization in deep learning may not be explainable by norms
Razin, N. and Cohen, N · 2020
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The implicit and explicit regularization effects of dropout, 2020
Wei, C., Kakade, S., and Ma, T · 2020
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Barlow twins: Self-supervised learning via redundancy reduction
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Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2021
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