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<p>Addressing the challenge of noisy labels in large datasets, this paper introduces a novel domain adaptation method leveraging comparative distillation model training. By transferring knowledge from a source domain model to a target domain, we construct a class correlation matrix and obtain a noise transfer matrix through network training. This matrix is used to correct noisy labels, enhancing the model’s generalization and robustness. The singular value decomposition of the noise transfer matrix constrains its training, while Tsallis entropy smooths the model’s output probabilities. Experimental results on the Office-31 and Office-Home datasets demonstrate significant improvements in classification accuracy, showcasing the effectiveness of our approach.</p>

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Correcting noisy labels via comparative distillation: a domain adaptation approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月5日

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  • Yapei Feng,
  • Junhui Liu,
  • Hua Zhong

摘要

Addressing the challenge of noisy labels in large datasets, this paper introduces a novel domain adaptation method leveraging comparative distillation model training. By transferring knowledge from a source domain model to a target domain, we construct a class correlation matrix and obtain a noise transfer matrix through network training. This matrix is used to correct noisy labels, enhancing the model’s generalization and robustness. The singular value decomposition of the noise transfer matrix constrains its training, while Tsallis entropy smooths the model’s output probabilities. Experimental results on the Office-31 and Office-Home datasets demonstrate significant improvements in classification accuracy, showcasing the effectiveness of our approach.