Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss (1906.07413v2)

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.

• The paper introduces LDAM loss that adjusts decision margins inversely to the 1/4th root of class frequencies to boost minority class performance.
• The paper presents a Deferred Re-Weighting schedule that delays re-weighting until after initial feature learning for more effective training.
• Experimental results on CIFAR-10/100, Tiny ImageNet, and iNaturalist 2018 demonstrate significant accuracy improvements over standard methods.

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

The paper "Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss" by Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma addresses the challenge of training deep learning models on imbalanced datasets, which is prevalent in many real-world scenarios.

Problem Statement

One significant issue in deep learning is handling class-imbalanced datasets where some classes have significantly more samples than others. Standard training procedures with cross-entropy loss often result in poor performance for minority classes, as these classes have less influence on the learning process. The authors propose two complementary solutions: a Label-Distribution-Aware Margin (LDAM) loss and a Deferred Re-Weighting (DRW) optimization schedule.

Contributions

1. Label-Distribution-Aware Margin (LDAM) Loss:

The LDAM loss directly addresses the imbalance by adjusting the decision boundary margins based on class frequencies. This theoretically grounded approach minimizes a margin-based generalization bound. Specifically, the margin for each class is inversely proportional to the 1/4th root of its sample size. By encouraging larger margins for minority classes, LDAM regularizes their decision boundaries more strongly, boosting their generalization performance.

2. Deferred Re-Weighting (DRW) Optimization Schedule:

The DRW schedule aims to refine the model training process by delaying the application of re-weighting until later in the training. Initially, the model is trained under the standard empirical risk minimization (ERM) framework, allowing it to learn robust feature representations. After the first learning rate decay, re-weighting is applied to balance the class importance more effectively.

Theoretical Foundations

The paper provides a solid theoretical foundation by deriving class-specific generalization error bounds. The bounds establish that the per-class generalization error depends on the margin and the sample size of each class. Optimizing these bounds within a margin-based framework gives rise to the LDAM loss function, which ensures optimal trade-offs between the margins of different classes, especially for binary classification.

Experimental Validation

The proposed methods were evaluated on a variety of benchmark datasets, including CIFAR-10, CIFAR-100, Tiny ImageNet, and iNaturalist 2018. Key findings include:

• CIFAR-10 and CIFAR-100: The combination of LDAM loss with DRW (LDAM-DRW) achieved significant performance improvements over other state-of-the-art methods, reducing top-1 validation error across various imbalance ratios.
• Tiny ImageNet: On this large-scale dataset with 200 classes, LDAM-DRW outperformed competing techniques in both long-tailed and step imbalance scenarios.
• iNaturalist 2018: The LDAM-DRW method notably improved top-1 and top-5 accuracy, demonstrating the approach's effectiveness in real-world large-scale imbalanced datasets.

Practical Implications

LDAM-DRW’s ability to handle long-tailed distributions and ensure fair performance across all classes has profound implications for applications requiring balanced predictions across a wide label spectrum. This includes fields like biology, where datasets often naturally follow a long-tailed distribution, and fairness-driven applications, where equal performance across classes is crucial.

Future Directions

Future research could explore the optimization dynamics of the DRW schedule to further amplify its benefits. Additionally, exploring LDAM loss in conjunction with other regularization techniques and architecture variations in deep learning models might yield further gains. Domain adaptation and transfer learning scenarios where test distributions differ significantly from training distributions also present intriguing avenues for applying and extending this work.

Conclusion

The LDAM loss and DRW schedule proposed in this paper offer a robust solution to the problem of learning from imbalanced datasets. Their combined application leads to substantial improvements across benchmarks and real-world datasets, backed by strong theoretical justifications. These contributions mark a significant step forward in developing more balanced and fair deep learning models suited to imbalanced data regimes.