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mixup: Beyond Empirical Risk Minimization
TLDR
Mixup as discussed by the authors trains a neural network on convex combinations of pairs of examples and their labels, and regularizes the neural network to favor simple linear behavior in between training examples, which improves the generalization of state-of-the-art neural network architectures.Abstract:
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.read more
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Proceedings ArticleDOI
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
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Proceedings ArticleDOI
AutoAugment: Learning Augmentation Strategies From Data
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Supervised Contrastive Learning.
Prannay Khosla,Piotr Teterwak,Chen Wang,Aaron Sarna,Yonglong Tian,Phillip Isola,Aaron Maschinot,Ce Liu,Dilip Krishnan +8 more
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References
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Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
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