On the Transferability and Discriminability of Repersentation Learning in Unsupervised Domain Adaptation
This work addresses the problem of improving unsupervised domain adaptation for machine learning practitioners by providing a more effective framework, though it is incremental as it builds on existing adversarial-based methods.
The paper tackled the limitation of relying solely on distribution alignment in unsupervised domain adaptation by showing it neglects target-domain discriminability, leading to suboptimal performance; they proposed a novel framework integrating discriminability-enhancing constraints, which consistently surpassed state-of-the-art methods across multiple benchmark datasets.
In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our information-theoretic analysis showed that this standard adversarial-based framework neglects the discriminability of target-domain features, leading to suboptimal performance. To bridge this theoretical-practical gap, we defined "good representation learning" as guaranteeing both transferability and discriminability, and proved that an additional loss term targeting target-domain discriminability is necessary. Building on these insights, we proposed a novel adversarial-based UDA framework that explicitly integrates a domain alignment objective with a discriminability-enhancing constraint. Instantiated as Domain-Invariant Representation Learning with Global and Local Consistency (RLGLC), our method leverages Asymmetrically-Relaxed Wasserstein of Wasserstein Distance (AR-WWD) to address class imbalance and semantic dimension weighting, and employs a local consistency mechanism to preserve fine-grained target-domain discriminative information. Extensive experiments across multiple benchmark datasets demonstrate that RLGLC consistently surpasses state-of-the-art methods, confirming the value of our theoretical perspective and underscoring the necessity of enforcing both transferability and discriminability in adversarial-based UDA.