Dynamic Double Classifiers Approximation for Cross-Domain Recognition.

Area of Science:

• Machine Learning
• Computer Vision

Background:

• Existing cross-domain recognition methods often focus on individual components like feature representation or classifier parameters.
• A unified optimization objective integrating both aspects is lacking, limiting learning efficiency.

Purpose of the Study:

• To propose a novel cross-domain recognition algorithm framework that integrates feature representation adaptation and classifier learning.
• To enhance learning efficiency by addressing discrepancies in both conditional and marginal distributions between domains.

Main Methods:

• Reducing feature discrepancies by aligning conditional and marginal distributions across domains.
• Learning dual classifiers per domain that dynamically approximate each other.
• Employing a classifier fusion strategy for final model selection.

Main Results:

• The proposed method effectively reduces feature discrepancies, pulling data from different domains closer.
• Dual classifiers dynamically approximate, enabling robust learning.
• Experimental results demonstrate superior performance compared to state-of-the-art methods.

Conclusions:

• The integrated framework offers a more effective approach to cross-domain recognition.
• Simultaneous optimization of feature representation and classifier learning is crucial for improved performance.
• The proposed method sets a new benchmark in cross-domain recognition tasks.