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A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation.

Rakesh Kumar Sanodiya1, Leehter Yao1

  • 1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

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|August 9, 2020
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Summary
This summary is machine-generated.

This study introduces a new method, Subspace based Transfer Joint Matching with Laplacian Regularization (STJML), to bridge the gap between different image datasets. STJML improves machine learning model performance on varied visual data by matching features and re-weighting instances.

Keywords:
classificationdomain adaptationfeature learninginstance re-weightingtransfer learningunsupervised discriminant analysis

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Area of Science:

  • Computer Science
  • Machine Learning
  • Computer Vision

Background:

  • Real-world image data often exhibits variations in illumination, resolution, pose, and blur due to different cameras and conditions.
  • These variations create a significant distribution gap between training (source) and testing (target) datasets, challenging standard machine learning algorithms like k-Nearest Neighbor (k-NN) and k-means.
  • Addressing this domain shift is crucial for effective visual domain adaptation.

Purpose of the Study:

  • To propose a novel method, Subspace based Transfer Joint Matching with Laplacian Regularization (STJML), for visual domain adaptation.
  • To minimize the distribution gap between source and target domains in image data.
  • To enhance the performance of machine learning models on target domain data despite variations from the source domain.

Main Methods:

  • The STJML method jointly matches features and re-weights instances across different domains.
  • It incorporates four key components: considering subspaces of both domains, instance re-weighting, reducing both marginal and conditional distribution shifts, and preserving data point similarity using Laplacian regularization.
  • This approach aims to minimize the distribution gap effectively.

Main Results:

  • Experiments were conducted on three popular real-world domain adaptation datasets.
  • The proposed STJML method demonstrated significant performance improvements compared to existing state-of-the-art methods.
  • The results validate the effectiveness of STJML in handling visual domain adaptation challenges.

Conclusions:

  • The STJML method offers a robust solution for visual domain adaptation by effectively bridging the distribution gap between datasets.
  • The joint matching of features and instance re-weighting, combined with Laplacian regularization, leads to superior performance.
  • This approach holds promise for improving machine learning applications dealing with diverse and varied image data.