  Data augmentation policies drastically improve the performance of image
recognition tasks, especially when the policies are optimized for the target
data and tasks. In this paper, we propose to optimize image recognition models
and data augmentation policies simultaneously to improve the performance using
gradient descent. Unlike prior methods, our approach avoids using proxy tasks
or reducing search space, and can directly improve the validation performance.
Our method achieves efficient and scalable training by approximating the
gradient of policies by implicit gradient with Neumann series approximation. We
demonstrate that our approach can improve the performance of various image
classification tasks, including ImageNet classification and fine-grained
recognition, without using dataset-specific hyperparameter tuning.
