  Mixup-based data augmentation has achieved great success as regularizer for
deep neural networks. However, existing mixup methods require explicitly
designed mixup policies. In this paper, we present a flexible, general
Automatic Mixup (AutoMix) framework which utilizes discriminative features to
learn a sample mixing policy adaptively. We regard mixup as a pretext task and
split it into two sub-problems: mixed samples generation and mixup
classification. To this end, we design a lightweight mix block to generate
synthetic samples based on feature maps and mix labels. Since the two
sub-problems are in the nature of Expectation-Maximization (EM), we also
propose a momentum training pipeline to optimize the mixup process and mixup
classification process alternatively in an end-to-end fashion. Extensive
experiments on six popular classification benchmarks show that AutoMix
consistently outperforms other leading mixup methods and improves
generalization abilities to downstream tasks. We hope AutoMix will motivate the
community to rethink the role of mixup in representation learning. The code
will be released soon.
