Source Free Robust Domain Adaptation Based on Pseudo Label Uncertainty Estimation

Unsupervised domain adaptation is one of the effective ways to solve the inconsistent distribution of training set (source domain) and test set (target domain). Existing unsupervised domain adaptation theories and methods have achieved some success in relatively closed and static environments. However, for open dynamic task environments, the robustness of existing unsupervised domain adaptation methods will face serious challenges under the constraints of privacy protection and data silos, where source domain data are often not directly accessible. In view of this, this paper investigates a more challenging yet under-explored problem: source free unsupervised domain adaptation, with the goal of achieving positive transfer from the source domain to the target domain based only on the pre-trained source domain model and unlabeled target domain data. In this paper, we propose a method called PLUE-SFRDA (pseudo label uncertainty estimation for source free robust domain adaptation). The core idea of PLUE-SFRDA is to combine information entropy and energy function to fully explore the implicit information of the target domain data based on the prediction results of the source domain model, explore the class prototypes and class anchors to accurately estimate the pseudo label of the target domain data, and then tune the domain adaptation model to achieve the source free robust domain adaptation. PLUE-SFRDA contains a proposed binary soft constraint information entropy, which solves the problem that the standard information entropy cannot effectively estimate the pseudo label uncertainty of samples at the decision boundary, enhances the confidence of the mined class prototypes, and thus improves the accuracy of pseudo label estimation in the target domain. PLUE-SFRDA contains a weighted comparison filtering method proposed by this paper. By comparing the weighted distances of each sample to the class anchors of other classes, the fuzzy samples of class information at the decision boundary are filtered out, which further improves the security of the new pseudo label uncertainty estimation. PLUE-SFRDA also contains an information maximization loss to achieve iterative optimization of the source domain classifier and the pseudo label estimator, which gradually migrates the source domain knowledge embedded in the source domain model to the target domain, further improving the robustness of the pseudo label uncertainty estimation. Extensive experiments on three publicly available datasets, Office-31, Office-Home and VisDA-C, show that PLUE-SFRDA not only outperforms the state-of-the-art source-free domain adaptation methods but also significantly outperforms standard domain adaptation methods which depend on the source-domain data. © Copyright 2022, Institute of Software, the Chinese Academy of Sciences. All rights reserved.