Iterative joint classifier and domain adaptation for visual transfer learning

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.