Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

TL;DR

This paper introduces a theoretically grounded margin loss and a training schedule to improve deep learning performance on imbalanced datasets, demonstrating significant gains on benchmarks like iNaturalist 2018.

Contribution

The paper presents a novel label-distribution-aware margin loss and a deferred re-weighting training schedule for better handling class imbalance in deep learning.

Findings

The LDAM loss improves generalization on minority classes.

Deferred re-weighting enhances training stability and accuracy.

Combined methods outperform existing techniques on benchmarks.

Abstract

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound. This loss replaces the standard cross-entropy objective during training and can be applied with prior strategies for training with class-imbalance such as re-weighting or re-sampling. Second, we propose a simple, yet effective, training schedule that defers re-weighting until after the initial stage, allowing the model to learn an initial representation while avoiding some of the complications associated with re-weighting or re-sampling. We test our methods on several benchmark vision tasks including the real-world imbalanced dataset iNaturalist 2018. Our experiments show that either of these methods alone can already improve over existing techniques and their combination achieves even better performance gains.