UC-SFDA: Source-free domain adaptation via uncertainty prediction and evidence-based contrastive learning

Abstract

Most unsupervised domain adaptation approaches learn domain-invariant features assuming that source and target domain data are available simultaneously. In practice, the availability of source samples is only sometimes possible. This paper establishes a novel source-free domain adaptation (SFDA) framework based on uncertainty prediction and a neighborhood-guided evidence-based contrastive learning scheme. First, we develop an evidence analyzer based on the uncertainty prediction principle of Dempster–Shafer (D–S) evidence theory, which improves the network capability for discriminating different types of samples. The transformer layer with a self-attention module is adopted to capture long-distance feature dependencies such that the proposed network has better generalization ability on multiple domains. Then, we offer a high-confidence target domain sample (HCS) acquisition strategy through evidence theory, entropy criterion, and distance information. A joint confidence enhancement scheme obtains the final HCS that generates pseudo-labels. Finally, we propose an optimization method based on evidence theory, evidence-based comparative learning, and internal neighborhood structure to ensure the separability between classes and compactness within categories. Experimental results show that the proposed framework performs superiorly on two standard datasets on multiple adaptation tasks. The code for this project is available at github.com/oolown/UC-SFDA.