Rethinking Maximum Mean Discrepancy for Visual Domain Adaptation.

Area of Science:

• Machine Learning
• Computer Vision
• Domain Adaptation

Background:

• Existing domain adaptation methods often balance distribution differences and discriminative structures using Maximum Mean Discrepancy (MMD) and class-specific distances.
• Current approaches empirically tune trade-offs between these losses, lacking a deep understanding of their interrelations, which can degrade model performance.

Purpose of the Study:

• To theoretically analyze the relationship between MMD, intra-class, and inter-class distances in domain adaptation.
• To propose a novel discriminative MMD approach that addresses the degradation of feature discriminability and intra-class distance expansion.

Main Methods:

• Theoretically proved that minimizing MMD can degrade feature discriminability by maximizing intra-class distances.
• Established an inverse relationship between intra-class and inter-class distances.
• Introduced a novel discriminative MMD with two parallel strategies: direct trade-off parameter on intra-class distance and reformulated inter-class distance with special weights.

Main Results:

• Demonstrated that minimizing MMD can inadvertently increase intra-class distances, harming feature discriminability.
• Experimental results on benchmark datasets validate the theoretical findings.
• The proposed discriminative MMD significantly outperforms state-of-the-art methods.

Conclusions:

• The study provides crucial theoretical insights into the interplay of distribution alignment and feature discrimination losses.
• The novel discriminative MMD effectively mitigates performance degradation caused by existing methods.
• The proposed approach offers a more robust and effective solution for domain adaptation tasks.