Unsupervised Domain Adaptation Method Based on Relative Entropy Regularization and Measure Propagation

Unsupervised Domain Adaptation Method Based on Relative Entropy Regularization and Measure Propagation

This paper presents a novel unsupervised domain adaptation (UDA) framework that integrates information-theoretic principles to mitigate distributional discrepancies between source and target domains. The proposed method incorporates two key components: (1) relative entropy regularization, which leve...

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Bibliographic Details
Main Authors: Lianghao Tan, Zhuo Peng, Yongjia Song, Xiaoyi Liu, Huangqi Jiang, Shubing Liu, Weixi Wu, Zhiyuan Xiang
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Entropy
Subjects:
unsupervised domain adaptation
information theory
relative entropy regularization
probability measure
Online Access:https://www.mdpi.com/1099-4300/27/4/426
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