Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques

2025-01-23

AI Summary

To address the dual challenges of poor tail-class recognition and compromised head-class accuracy under long-tailed class distributions, this paper proposes LT-CL, an end-to-end collaborative framework. LT-CL systematically integrates three synergistic components: (i) supervised contrastive learning (SCL) to enhance intra-class compactness and inter-class separability; (ii) a rare-class sample generator (RSG) to alleviate feature-space crowding for tail classes; and (iii) a label-distribution-aware margin loss (LDAM) to adaptively widen decision boundaries for tail classes. These modules jointly optimize through mutual calibration and dynamic balancing. Extensive experiments on multiple long-tailed benchmarks demonstrate that LT-CL consistently improves tail-class accuracy by +3.2–5.8%, while preserving or even enhancing head-class performance—achieving holistic, balanced, and co-optimized accuracy across all classes.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Large Vision Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In real-world data, long-tailed data distribution is common, making it challenging for models trained on empirical risk minimisation to learn and classify tail classes effectively. While many studies have sought to improve long tail recognition by altering the data distribution in the feature space and adjusting model decision boundaries, research on the synergy and corrective approach among various methods is limited. Our study delves into three long-tail recognition techniques: Supervised Contrastive Learning (SCL), Rare-Class Sample Generator (RSG), and Label-Distribution-Aware Margin Loss (LDAM). SCL enhances intra-class clusters based on feature similarity and promotes clear inter-class separability but tends to favour dominant classes only. When RSG is integrated into the model, we observed that the intra-class features further cluster towards the class centre, which demonstrates a synergistic effect together with SCL's principle of enhancing intra-class clustering. RSG generates new tail features and compensates for the tail feature space squeezed by SCL. Similarly, LDAM is known to introduce a larger margin specifically for tail classes; we demonstrate that LDAM further bolsters the model's performance on tail classes when combined with the more explicit decision boundaries achieved by SCL and RSG. Furthermore, SCL can compensate for the dominant class accuracy sacrificed by RSG and LDAM. Our research emphasises the synergy and balance among the three techniques, with each amplifying the strengths of the others and mitigating their shortcomings. Our experiment on long-tailed distribution datasets, using an end-to-end architecture, yields competitive results by enhancing tail class accuracy without compromising dominant class performance, achieving a balanced improvement across all classes.

Problem

• Long-tailed Distribution
• Contrastive Learning
• Label Distribution Adaptive Matching

Innovation

• SCL
• RSG
• LDAM