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Preserving domain private information via mutual information maximization.

Jiahong Chen1, Jing Wang1, Weipeng Lin2

  • 1Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.

Neural Networks : the Official Journal of the International Neural Network Society
|January 13, 2024
PubMed
Summary

This study introduces a new unsupervised domain adaptation method that preserves unique data characteristics. By maximizing mutual information, it enhances model generalization across different datasets.

Keywords:
Computer visionDeep learningDomain adaptationInformation theory

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • Unsupervised domain adaptation (UDA) aims to improve model generalization to new data domains.
  • Current UDA methods focus on domain-invariant features but often discard valuable domain-specific information.
  • This domain-specific information is crucial for robust cross-domain generalization.

Purpose of the Study:

  • To propose a novel UDA method that preserves domain-private information while ensuring domain-invariant features.
  • To enhance cross-domain generalization by retaining unique statistics of the unlabeled target domain.
  • To validate the effectiveness of preserving domain-specific information for UDA.

Main Methods:

  • Utilizing mutual information to protect domain-specific information in latent features.
  • Simultaneously maximizing mutual information and minimizing domain divergence.
  • Employing a neural estimator to quantify mutual information between input and latent spaces.

Main Results:

  • The proposed method effectively preserves domain-private information, leading to improved generalization.
  • Simultaneous optimization of mutual information and domain divergence is shown to be effective.
  • Theoretical analysis and empirical results confirm the significance of preserving unique domain information.

Conclusions:

  • Preserving domain-private information is critical for superior cross-domain generalization in UDA.
  • The novel method outperforms existing state-of-the-art techniques on benchmark datasets.
  • This work offers a new direction for developing more effective UDA models.