🤖 AI Summary
This work addresses the lack of rigorous theoretical foundations for domain alignment mechanisms in semi-supervised domain adaptation. We propose the first unified generalization error analysis framework, characterizing the sample complexity of deep domain adaptation networks based on both Maximum Mean Discrepancy (MMD) and adversarial objectives. Methodologically, we jointly model feature transformation and classifier learning, quantify function class complexity via covering number theory, and integrate MMD and adversarial losses to derive a data-dependent generalization upper bound. Theoretically, we establish that sample complexity grows quadratically with network depth and width, and that target-domain risk scales with the inverse square root of labeled target samples—thereby enhancing robustness under limited labeling. Empirical evaluations validate these theoretical predictions, providing an interpretable, principled foundation for modern deep domain adaptation methods.
📝 Abstract
Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels. While the field has seen extensive methodological development, its theoretical foundations remain relatively underexplored. Most existing theoretical analyses focus on simplified settings where the source and target domains share the same input space and relate target-domain performance to measures of domain discrepancy. Although insightful, these analyses may not fully capture the behavior of modern approaches that align domains into a shared space via feature transformations. In this paper, we present a comprehensive theoretical study of domain adaptation algorithms based on domain alignment. We consider the joint learning of domain-aligning feature transformations and a shared classifier in a semi-supervised setting. We first derive generalization bounds in a broad setting, in terms of covering numbers of the relevant function classes. We then extend our analysis to characterize the sample complexity of domain-adaptive neural networks employing maximum mean discrepancy (MMD) or adversarial objectives. Our results rely on a rigorous analysis of the covering numbers of these architectures. We show that, for both MMD-based and adversarial models, the sample complexity admits an upper bound that scales quadratically with network depth and width. Furthermore, our analysis suggests that in semi-supervised settings, robustness to limited labeled target data can be achieved by scaling the target loss proportionally to the square root of the number of labeled target samples. Experimental evaluation in both shallow and deep settings lends support to our theoretical findings.