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Visualizing Visual Adaptation
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Iterative joint classifier and domain adaptation for visual transfer learning.

Shiva Noori Saray1, Jafar Tahmoresnezhad1

  • 1Faculty of Information Technology and Computer Engineering, Urmia University of Technology, Urmia, Iran.

International Journal of Machine Learning and Cybernetics
|October 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel transfer learning framework (ICDAV) to improve classifier generalization across different domains. The method enhances visual domain adaptation by effectively transferring knowledge and adapting data distributions.

Keywords:
Domain adaptationManifold regularizationTransfer learningVisual classification

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Supervised classifiers struggle with generalization due to distribution mismatch across domains.
  • Domain shift, caused by varying data collection conditions, necessitates advanced adaptation techniques.

Purpose of the Study:

  • To propose a novel transfer learning framework, Iterative Joint Classifier and Domain Adaptation for Visual Transfer Learning (ICDAV).
  • To enhance the generalization capability of classifiers in visual domain adaptation tasks.

Main Methods:

  • Utilizing balanced maximum mean discrepancy for improved knowledge transfer.
  • Employing graph manifold regularizer and modified joint probability maximum mean discrepancy for robust classification.
  • Simultaneously capturing domain structures and adapting projected sample distributions.

Main Results:

  • ICDAV demonstrates remarkable performance in visual domain adaptation.
  • The framework effectively addresses the domain shift problem in transfer learning.
  • Experiments on public datasets validate the approach's efficacy.

Conclusions:

  • The proposed ICDAV framework significantly improves visual transfer learning.
  • The methods employed are effective in mitigating domain shift challenges.
  • This research contributes a robust solution for cross-domain classification problems.