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TIToK: A solution for bi-imbalanced unsupervised domain adaptation.

Yunyun Wang1, Quchuan Chen2, Yao Liu1

  • 1School of Computer Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 6, 2023
PubMed
Summary

This study introduces TIToK, a novel method for unsupervised domain adaptation (UDA) that tackles class imbalance in both source and target domains. TIToK effectively transfers knowledge across imbalanced datasets, improving model performance and robustness.

Keywords:
Class contrastive knowledgeImbalanced learningSemi-supervised learningUnsupervised domain adaptation

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Unsupervised Domain Adaptation (UDA) typically assumes balanced data distributions.
  • Real-world UDA scenarios often involve class imbalance within and across domains (bi-imbalanced data).
  • Existing methods like source re-weighting may fail due to unknown target label distributions.

Purpose of the Study:

  • To address the challenges of bi-imbalanced data in UDA.
  • To propose a novel approach for transferring knowledge robustly across domains with differing class imbalance ratios.
  • To improve the performance and reliability of UDA models in practical applications.

Main Methods:

  • Proposed TIToK (Transferring Imbalance-Tolerant Knowledge) for bi-imbalanced UDA.
  • Introduced a class contrastive loss to reduce sensitivity to imbalance during knowledge transfer.
  • Incorporated transfer of class correlation knowledge, which is imbalance-invariant.
  • Developed discriminative feature alignment for robust classifier boundaries.

Main Results:

  • TIToK achieved competitive performance compared to state-of-the-art methods on benchmark datasets.
  • The proposed method demonstrated reduced sensitivity to class imbalance.
  • Effectively transferred knowledge despite significant differences in data distributions.

Conclusions:

  • TIToK offers a robust solution for UDA in bi-imbalanced settings.
  • The approach enhances model generalization by mitigating the negative impacts of class imbalance.
  • TIToK provides a more reliable method for knowledge transfer in practical, imbalanced UDA tasks.