mlsanityData to 7 Oct 2026

mixup: Beyond Empirical Risk Minimization

arXiv 1710.09412 ↗First posted 25 Oct 2017cs.LG stat.MLcited by 5,155

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Cited by 126 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 5,155 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Oct 2017.

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Citing papers per year: 2020: 1; 2021: 875; 2022: 1114; 2023: 1133; 2024: 963; 2025: 648; 2026: 421.

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