Decoupled Mixup for Data-efficient Learning

Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup methods improve previous static policies (e.g., linear interpolation) by maximizing discriminative regions or maintaining the salient objects in mixed samples. We notice that The mixed samples from dynamic policies are more separable than the static ones while preventing models from overfitting. Inspired by this finding, we first argue that there exists an over-smoothing issue in the mixup objective, which focuses on regression the mixing ratio instead of identifying discriminative features. We are therefore prompted to propose a decoupled mixup (DM) loss that can adaptively mine discriminative features without losing smoothness. DM enables static mixup methods to achieve comparable performance with dynamic methods while avoiding heavy computational overhead. This also leads to an interesting objective design problem for mixup training that we need to focus not only on smoothing the decision boundaries but also on identifying discriminative features. Extensive experiments on supervised and semi-supervised learning benchmarks across seven classification datasets validate the effectiveness of DM by equipping with various mixup methods.