Systematic Analysis of Source-Free Domain Adaptation Methods

Source-Free Domain Adaptation (SFDA) aims to address the challenge of effectively transferring a source domain model to a target domain when the target domain data is unlabeled and the source domain data is unavailable. Traditional Unsupervised Domain Adaptation (UDA) methods rely on simultaneous access to both source and target domain data. However, in many practical scenarios, such as medical data privacy protection or resource-constrained devices, direct access to source domain data is not feasible. SFDA leverages only a pre-trained source domain model and unlabeled target domain data to update the model, avoiding the direct use of source domain data and thereby meeting privacy and security requirements. This paper provides a systematic classification and review of SFDA research methods, categorizing them into three main types: data-related methods, model-related methods, and loss-related methods. Data-related methods replace missing source data by extracting data or feature augmentation information from pre-trained models; model-related methods reduce domain discrepancies by optimizing feature representations or utilizing information in the feature space; and loss-related methods enhance the model's generalization ability through specific loss functions. This paper aims to offer a clear research roadmap for researchers in the field by systematically classifying and analyzing existing SFDA methods, facilitating the selection of appropriate methods or the development of new strategies to address specific problems.