Data augmentation (DA) plays a critical role in improving the generalization
of deep learning models. Recent works on automatically searching for DA
policies from data have achieved great success. However, existing automated DA
methods generally perform the search at the image level, which limits the
exploration of diversity in local regions. In this paper, we propose a more
fine-grained automated DA approach, dubbed Patch AutoAugment, to divide an
image into a grid of patches and search for the joint optimal augmentation
policies for the patches. We formulate it as a multi-agent reinforcement
learning (MARL) problem, where each agent learns an augmentation policy for
each patch based on its content together with the semantics of the whole image.
The agents cooperate with each other to achieve the optimal augmentation effect
of the entire image by sharing a team reward. We show the effectiveness of our
method on multiple benchmark datasets of image classification and fine-grained
image recognition (e.g., CIFAR-10, CIFAR-100, ImageNet, CUB-200-2011, Stanford
Cars and FGVC-Aircraft). Extensive experiments demonstrate that our method
outperforms the state-of-the-art DA methods while requiring fewer computational
resources.

本文提出了一种更细致的自动数据增强方法，称为 Patch AutoAugment，它能够在局部区域内寻找最佳数据增强策略，并通过多智能体强化学习协作实现对整张图片的优化增强效果，实验结果表明，该方法在多个基准数据集上具有比其他最新数据增强方法更好的性能表现，且需要更少的计算资源。