Abstract: Domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain,thereby improving model performance on the target domain while reducing the need for target domain data annotation.As a more realistic extension,partial domain adaptation(PDA) relaxes the assumption of complete label space sharing between the source and target domains,and handles the case where the target label space is a subset of the source label space.The proposed machine unlearning method helps to address the challenging problem of partial domain adaptation by forgetting outlier-weighted categories.Specifically,the method firstly uses a traditional PDA method as the initialization model,while the category weight mechanism identifies the outlier-weighted categories.Then,it screens the source domain dataset based on the outlier-weighted categories and generates a noisy sample dataset,and then performs unlearning on the model to solve the label space mismatch problem between the source and target domains.Finally,it leverages pseudo-labeling techniques to further align the feature distribution of the target domain,thereby promoting positive transfer.Extensive experiments on the publicly available Office-31 and Office-Home benchmark datasets show that the proposed machine unlearning method not only performs on par with the latest PDA methods,but also significantly outperforms traditional PDA methods.

Key words: Transfer learning, Partial domain adaptation, Machine unlearning, Pseudo label, Negative transfer