Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data

• URL: http://arxiv.org/abs/2510.08179v1
• Date: Thu, 09 Oct 2025 13:05:27 GMT
• Title: Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data
• Authors: Feng Hong, Yu Huang, Zihua Zhao, Zhihan Zhou, Jiangchao Yao, Dongsheng Li, Ya Zhang, Yanfeng Wang,
• Abstract summary: Real-world datasets for deep learning frequently suffer from the co-occurring challenges of class imbalance and label noise.<n>We propose Dual-granularity Sinkhorn Distillation (D-SINK), a novel framework that enhances dual robustness by distilling and integrating complementary insights.<n>Experiments on benchmark datasets demonstrate that D-SINK significantly improves robustness and achieves strong empirical performance in learning from long-tailed noisy data.
• Score: 67.25796812343454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world datasets for deep learning frequently suffer from the co-occurring challenges of class imbalance and label noise, hindering model performance. While methods exist for each issue, effectively combining them is non-trivial, as distinguishing genuine tail samples from noisy data proves difficult, often leading to conflicting optimization strategies. This paper presents a novel perspective: instead of primarily developing new complex techniques from scratch, we explore synergistically leveraging well-established, individually 'weak' auxiliary models - specialized for tackling either class imbalance or label noise but not both. This view is motivated by the insight that class imbalance (a distributional-level concern) and label noise (a sample-level concern) operate at different granularities, suggesting that robustness mechanisms for each can in principle offer complementary strengths without conflict. We propose Dual-granularity Sinkhorn Distillation (D-SINK), a novel framework that enhances dual robustness by distilling and integrating complementary insights from such 'weak', single-purpose auxiliary models. Specifically, D-SINK uses an optimal transport-optimized surrogate label allocation to align the target model's sample-level predictions with a noise-robust auxiliary and its class distributions with an imbalance-robust one. Extensive experiments on benchmark datasets demonstrate that D-SINK significantly improves robustness and achieves strong empirical performance in learning from long-tailed noisy data.

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