Self-Adaptive Training: beyond Empirical Risk Minimization

• URL: http://arxiv.org/abs/2002.10319v2
• Date: Wed, 30 Sep 2020 09:14:50 GMT
• Title: Self-Adaptive Training: beyond Empirical Risk Minimization
• Authors: Lang Huang, Chao Zhang, Hongyang Zhang
• Abstract summary: We propose a new training algorithm that dynamically corrects problematic labels by model predictions without incurring extra computational cost. Self-adaptive training significantly improves generalization over various levels of noises, and mitigates the overfitting issue in both natural and adversarial training. Experiments on CIFAR and ImageNet datasets verify the effectiveness of our approach in two applications.
• Score: 15.59721834388181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost---to improve generalization of deep learning for potentially corrupted training data. This problem is crucial towards robustly learning from data that are corrupted by, e.g., label noises and out-of-distribution samples. The standard empirical risk minimization (ERM) for such data, however, may easily overfit noises and thus suffers from sub-optimal performance. In this paper, we observe that model predictions can substantially benefit the training process: self-adaptive training significantly improves generalization over ERM under various levels of noises, and mitigates the overfitting issue in both natural and adversarial training. We evaluate the error-capacity curve of self-adaptive training: the test error is monotonously decreasing w.r.t. model capacity. This is in sharp contrast to the recently-discovered double-descent phenomenon in ERM which might be a result of overfitting of noises. Experiments on CIFAR and ImageNet datasets verify the effectiveness of our approach in two applications: classification with label noise and selective classification. We release our code at https://github.com/LayneH/self-adaptive-training.

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