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Mixup is a popular data augmentation technique based on taking convex combinations of pairs of examples and their labels.
Estimation of dependences based on empirical data nauka, 1979
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On the inductive bias of dropout
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Norm-based capacity control in neural networks
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On adversarial mixup resynthesis
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Mixmatch: A holistic approach to semi-supervised learning
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Mixup as locally linear out-of-manifold regularization
Hongyu Guo, Yongyi Mao, and Richong Zhang · 2019
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Interpolated adversarial training: Achieving robust neural networks without sacrificing too much accuracy
Alex Lamb, Vikas Verma, Juho Kannala, and Yoshua Bengio · 2019
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Towards understanding the role of over-parametrization in generalization of neural networks
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Pytorch: An imperative style, high-performance deep learning library
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
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Dropout: Explicit forms and capacity control
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Patchup: A regularization technique for convolutional neural networks
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Puzzle mix: Exploiting saliency and local statistics for optimal mixup
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The implicit and explicit regularization effects of dropout
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