mixup: Beyond Empirical Risk Minimization

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Classification CIFAR-10 DenseNet-BC-190 + Mixup Percentage correct 97.3 # 70
PARAMS 25.6M # 168
Image Classification CIFAR-100 DenseNet-BC-190 + Mixup Percentage correct 83.20 # 73
Semi-Supervised Image Classification CIFAR-10, 250 Labels MixUp Percentage error 47.43 # 12
Domain Generalization ImageNet-A Mixup (ResNet-50) Top-1 accuracy % 6.6 # 11
Image Classification Kuzushiji-MNIST PreActResNet-18 + Input Mixup Accuracy 98.41 # 14
Semi-Supervised Image Classification SVHN, 250 Labels MixUp Accuracy 60.03 # 12