  Data augmentation (DA) plays a critical role in training deep neural networks
for improving the generalization of models. Recent work has shown that
automatic DA policy, such as AutoAugment (AA), significantly improves model
performance. However, most automatic DA methods search for DA policies at the
image-level without considering that the optimal policies for different regions
in an image may be diverse. In this paper, we propose a patch-level automatic
DA algorithm called Patch AutoAugment (PAA). PAA divides an image into a grid
of patches and searches for the optimal DA policy of each patch. Specifically,
PAA allows each patch DA operation to be controlled by an agent and models it
as a Multi-Agent Reinforcement Learning (MARL) problem. At each step, PAA
samples the most effective operation for each patch based on its content and
the semantics of the whole image. The agents cooperate as a team and share a
unified team reward for achieving the joint optimal DA policy of the whole
image. The experiment shows that PAA consistently improves the target network
performance on many benchmark datasets of image classification and fine-grained
image recognition. PAA also achieves remarkable computational efficiency, i.e
2.3x faster than FastAA and 56.1x faster than AA on ImageNet.
