  Data mixing augmentation have proved to be effective in improving the
generalization ability of deep neural networks. While early methods mix samples
by hand-crafted policies (e.g., linear interpolation), recent methods utilize
saliency information to match the mixed samples and labels via complex offline
optimization. However, there arises a trade-off between precise mixing policies
and optimization complexity. To address this challenge, we propose a novel
automatic mixup (AutoMix) framework, where the mixup policy is parameterized
and serves the ultimate classification goal directly. Specifically, AutoMix
reformulates the mixup classification into two sub-tasks (i.e., mixed sample
generation and mixup classification) with corresponding sub-networks and solves
them in a bi-level optimization framework. For the generation, a learnable
lightweight mixup generator, Mix Block, is designed to generate mixed samples
by modeling patch-wise relationships under the direct supervision of the
corresponding mixed labels. To prevent the degradation and instability of
bi-level optimization, we further introduce a momentum pipeline to train
AutoMix in an end-to-end manner. Extensive experiments on nine image benchmarks
prove the superiority of AutoMix compared with state-of-the-art in various
classification scenarios and downstream tasks.
