Align and Adapt: A Two-stage Adaptation Framework for Unsupervised Domain Adaptation

Abstract

Unsupervised domain adaptation aims to transfer knowledge from a labeled but heterogeneous source domain to an unlabeled target domain, alleviating the labeling efforts. Early advances in domain adaptation focus on invariant representations learning (IRL) methods to align domain distributions. Recent studies further utilize semi-supervised learning (SSL) methods to regularize domain-invariant representations based on the cluster assumption, making the category boundary more clear. However, the misalignment in the IRL methods might be intensified by SSL methods if the target instances are more proximate to the wrong source centroid, resulting in incompatibility between these techniques. In this paper, we hypothesize this phenomenon derives from the distraction of the source domain, and further give a novel two-stage adaptation framework to adapt the model toward the target domain. In addition, we propose DCAN to reduce the misalignment in IRL methods in the first stage, and we propose PCST to encode the semantic structure of unlabeled target data in the second stage. Extensive experiments demonstrate that our method outperforms current state-of-the-art methods on four benchmarks (Office-31, ImageCLEF-DA, Office-Home, and VisDA-2017).