C²DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Deep neural networks are effective for domain adaptation, leveraging labeled source data for target domains.
• Deep Adaptation Network (DAN) uses Multi-Kernel Maximum Mean Discrepancy (MK-MMD) for feature distribution alignment.
• Existing DAN methods face limitations in feature-level transfer and classifier assumptions across domains.

Purpose of the Study:

• To enhance the adaptability of Deep Adaptation Networks (DAN).
• To address limitations in feature transfer and classifier assumptions in domain adaptation.
• To propose a novel domain adaptation method, C²DAN, for improved cross-domain performance.

Main Methods:

• Incorporation of Domain Confusion (DC) via adversarial training with a domain discriminator.
• Implementation of Classifier Adaptation (CA) using a residual block to learn classifier differences.
• Development of the novel C²DAN framework integrating DC and CA.

Main Results:

• C²DAN demonstrates improved feature-level transfer compared to standard DAN.
• The proposed method effectively adapts classifiers between source and target domains.
• Experiments on Office-31 and Comprehensive Cars (CompCars) datasets validate C²DAN's effectiveness.

Conclusions:

• C²DAN significantly enhances domain adaptation capabilities by improving feature transfer and classifier adaptation.
• The novel integration of Domain Confusion and Classifier Adaptation leads to superior performance.
• The framework shows broad applicability and effectiveness across different domain adaptation scenarios.