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Label-Noise Robust Domain Adaptation.

Xiyu Yu1, Tongliang Liu2, Mingming Gong3

  • 1Department of Computer Vision Technology (VIS), Baidu Incorporation.

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Summary
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This study addresses noisy labels in source data for domain adaptation. A new framework, denoising Conditional Invariant Component (DCIC), effectively reduces label noise effects and improves domain adaptation performance.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • Domain adaptation (DA) methods aim to bridge distribution shifts between source and target domains.
  • Current DA techniques often assume clean labels in the source domain, which is unrealistic.
  • Label noise in the source domain can significantly degrade the performance of existing DA methods.

Purpose of the Study:

  • To investigate the impact of source domain label noise on various domain adaptation methods.
  • To theoretically prove the existence of a method robust to noisy source labels.
  • To propose a novel framework that mitigates the adverse effects of label noise in DA.

Main Methods:

  • Comprehensive analysis of label noise effects on DA algorithms.
  • Theoretical proof for a noise-robust DA method.
  • Development of the denoising Conditional Invariant Component (DCIC) framework.
  • Focus on the generalized target shift scenario.

Main Results:

  • The DCIC framework provably extracts invariant representations from noisy source data and unlabeled target data.
  • DCIC ensures unbiased estimation of the target domain's label distribution.
  • Experimental validation on synthetic and real-world datasets demonstrates DCIC's effectiveness.

Conclusions:

  • Label noise in the source domain is a critical challenge for domain adaptation.
  • The proposed DCIC framework offers a robust solution for domain adaptation with noisy source labels.
  • DCIC advances the field by providing theoretical guarantees and practical effectiveness.