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Data-Driven Approach to Multiple-Source Domain Adaptation.

Petar Stojanov1, Mingming Gong2, Jaime G Carbonell3

  • 1Computer Science Department, Carnegie Mellon University.

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Summary
This summary is machine-generated.

This study introduces a data-driven approach for unsupervised domain adaptation by identifying key changing parameters across multiple domains. This method effectively reconstructs target distributions and predicts labels using unlabeled data.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • Domain adaptation is crucial for applying models to new data distributions.
  • Identifying transferable knowledge across domains remains a significant challenge.
  • Current methods often struggle with unsupervised adaptation across multiple sources.

Purpose of the Study:

  • To develop a data-driven method for unsupervised domain adaptation.
  • To identify and represent changes across multiple source domains.
  • To enable accurate label prediction in target domains without labeled data.

Main Methods:

  • Proposed a method assuming a specific generating process for joint distributions.
  • Developed a data-driven technique to identify changing parameters.
  • Learned low-dimensional representations of class-conditional distributions across domains.

Main Results:

  • Successfully reconstructed target-domain joint distributions using unlabeled data.
  • Achieved accurate label prediction in the target domain.
  • Demonstrated efficacy on both synthetic and real-world datasets.

Conclusions:

  • The proposed method effectively addresses unsupervised domain adaptation.
  • Identifying changing parameters via low-dimensional representations is a viable strategy.
  • This approach offers a robust solution for knowledge transfer in machine learning.