Papers › mixup: Beyond Empirical Risk Minimization

mixup: Beyond Empirical Risk Minimization

25 Oct 2017ICLR 2018 1arXiv:1710.09412archive 2025-07-28

Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz

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.

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Methods

Introduced by this paper: Mixup

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