Maximum Structural Generation Discrepancy for Unsupervised Domain Adaptation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Unsupervised domain adaptation (UDA) aims to leverage labeled source data for models generalized to unlabeled target domains.
• A key challenge in UDA is learning domain-invariant features due to significant domain discrepancies.
• Existing methods struggle to effectively bridge the gap between source and target domains without target annotations.

Purpose of the Study:

• To propose a novel cross-domain learning framework, Maximum Structural Generation Discrepancy (MSGD), for UDA.
• To effectively estimate and mitigate domain shift by introducing an intermediate domain.
• To enhance the generalization capability of models in target domains without requiring target annotations.

Main Methods:

• MSGD utilizes cross-domain topological structure to propagate target samples and generate an intermediate domain.
• An intermediate domain acts as a bridge, facilitating gradual reduction of distribution divergence between source and target domains.
• The class-driven collaborative translation (CDCT) module generates class-consistent cross-domain samples using pseudo-labels for improved feature learning.

Main Results:

• The proposed MSGD framework effectively reduces domain discrepancy by aligning features at both class and domain levels.
• The intermediate domain facilitates semantic alignment across categories, mitigating domain shift.
• Experimental results on five benchmarks demonstrate the effectiveness of MSGD in unsupervised domain adaptation tasks.

Conclusions:

• MSGD offers a robust approach to unsupervised domain adaptation by introducing an intermediate domain for feature alignment.
• The framework successfully addresses the challenge of learning domain-invariant representations, crucial for UDA.
• MSGD shows significant potential for improving visual recognition models' performance on unlabeled target domains.