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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Unsupervised Domain Adaptation Method Based on Relative Entropy Regularization and Measure Propagation.

Lianghao Tan1, Zhuo Peng1, Yongjia Song2

  • 1Department of Computer Science, Arizona State University, Tempe, AZ 85281, USA.

Entropy (Basel, Switzerland)
|April 26, 2025
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This study introduces a new unsupervised domain adaptation framework using information theory to reduce domain discrepancies. The method improves feature alignment and semantic consistency, outperforming existing approaches on benchmark datasets.

Keywords:
information theoryprobability measurerelative entropy regularizationunsupervised domain adaptation

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Unsupervised domain adaptation (UDA) is crucial for applying models to new data distributions.
  • Distributional discrepancies between source and target domains hinder model generalization.
  • Existing UDA methods often struggle with significant domain shifts.

Purpose of the Study:

  • To propose a novel UDA framework integrating information-theoretic principles.
  • To mitigate distributional discrepancies between source and target domains effectively.
  • To enhance both global feature alignment and semantic consistency.

Main Methods:

  • Relative entropy regularization using Kullback-Leibler (KL) divergence to align label distributions.
  • Measure propagation to transfer probability mass and create pseudo-measures for the target domain.
  • A dual mechanism combining these components for robust adaptation.

Main Results:

  • The proposed framework consistently outperforms State-of-the-Art methods on OfficeHome and DomainNet datasets.
  • Superior performance is observed, especially in scenarios with significant domain shifts.
  • Demonstrated robustness, scalability, and theoretical grounding of the UDA framework.

Conclusions:

  • The novel UDA framework effectively reduces domain distributional discrepancies.
  • The integration of information-theoretic principles offers a new perspective on domain adaptation.
  • The method shows significant improvements in cross-domain generalization capabilities.