Fine-Grained AutoAugmentation for Multi-Label Classification

TL;DR

This paper introduces Label-Based AutoAugmentation (LB-Aug), a novel method that generates label-specific augmentation policies for multi-label classification, leading to significant improvements over existing methods.

Contribution

The paper proposes a reinforcement learning-based approach to generate label-specific augmentation policies, addressing the limitations of unified policies in multi-label tasks.

Findings

LB-Aug outperforms state-of-the-art augmentation methods

Significant improvements on multiple image and video classification benchmarks

Policies tailored to labels benefit multi-label classification performance

Abstract

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.