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.
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.
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.
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