Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better generalization than hand-crafted ones. However, most of these works use unified augmentation policies for all samples in a dataset, which is observed not necessarily beneficial for all labels in multi-label classification tasks, i.e., some policies may have negative impacts on some labels while benefitting the others. To tackle this problem, we propose a novel Label-Based AutoAugmentation (LB-Aug) method for multi-label scenarios, where augmentation policies are generated with respect to labels by an augmentation-policy network. The policies are learned via reinforcement learning using policy gradient methods, providing a mapping from instance labels to their optimal augmentation policies. Numerical experiments show that our LB-Aug outperforms previous state-of-the-art augmentation methods by large margins in multiple benchmarks on image and video classification.
@article{arxiv.2107.05384,
title = {Fine-Grained AutoAugmentation for Multi-Label Classification},
author = {Ya Wang and Hesen Chen and Fangyi Zhang and Yaohua Wang and Xiuyu Sun and Ming Lin and Hao Li},
journal= {arXiv preprint arXiv:2107.05384},
year = {2021}
}