Mixup: Beyond empirical risk minimization

Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of advanced neural network architectures.

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• 1 In this work, we propose mixup, a simple learning principle to alleviate these issues.
• 2 In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels.
• 3 By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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