Fine-Grained AutoAugmentation for Multi-Label Classification
This addresses the need for more effective data augmentation in multi-label classification tasks, offering a novel approach that improves generalization over existing methods.
The paper tackles the problem of unified data augmentation policies being suboptimal for multi-label classification by proposing a label-based AutoAugmentation method that generates policies per label via reinforcement learning. It achieves state-of-the-art performance with large margins on image and video benchmarks.
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.