Over-Training with Mixup May Hurt GeneralizationDownload PDF

06 Oct 2022 (modified: 05 May 2023)INTERPOLATE at NeurIPS 2022Readers: Everyone
Keywords: Mixup, Generalization, Overfitting, Regularization
Abstract: Mixup, which creates synthetic training instances by linearly interpolating random sample pairs, is a simple yet effective regularization technique to boost the performance of deep models trained with SGD. In this work, we report a previously unobserved phenomenon in Mixup training: on a number of standard datasets, the performance of Mixup-trained models starts to decay after training for a large number of epochs, giving rise to a U-shaped generalization curve. This behavior is further aggravated when the size of the original dataset is reduced. To help understand such a behavior of Mixup, we show theoretically that Mixup training may introduce undesired data-dependent label noises to the synthesized data. Via analyzing a least-square regression problem with a random feature model, we explain why noisy labels may cause the U-shaped curve to occur: Mixup improves generalization through fitting the clean patterns at the early training stage, but as training progresses, Mixup becomes over-fitting to the noise in the synthetic data.
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