Collective domain adversarial learning for unsupervised domain adaptation

Abstract

Recent works in Unsupervised Domain Adaptation mainly focus on either divergence-based or adversarial methods. Divergence-based approaches minimize domain discrepancy by selecting an appropriate divergence measure, although the optimal choice can be task-specific in practice. On the other hand, adversarial methods aim to extract domain-invariant features by enforcing indistinguishability between domains in a Min-Max adversarial framework, neglecting the sample correlations. To overcome this limitation, we propose a novel adversarial domain adaptation framework that leverages the collective assumption to model and exploit higher-order interactions among samples. By capturing these collective domain features, our method achieves a more robust domain alignment, demonstrating enhanced resilience to noise and domain ambiguity. Furthermore, experimental results demonstrate that our approach achieves consistent improvements over conventional adversarial training techniques and can seamlessly integrate with existing domain adaptation strategies in a plug-and-play manner, offering a valuable contribution towards advancing state-of-the-art performance.

Graphical abstract

Keywords

unsupervised domain adaptation / adversarial training / collective assumption

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Higher Education Press