Source-Free Active Domain Adaptation via Energy-Based Locality Preserving Transfer

Unsupervised domain adaptation (UDA) aims at transferring knowledge from one labeled source domain to a related but unlabeled target domain. Recently, active domain adaptation (ADA) has been proposed as a new paradigm which significantly boosts performance of UDA with minor additional labeling. However, existing ADA methods require source data to explicitly measure the domain gap between the source domain and the target domain, which is restricted in many real-world scenarios. In this work, we handle ADA with only a source-pretrained model and unlabeled target data, proposing a new setting named source-free active domain adaptation. Specifically, we propose a Locality Preserving Transfer (LPT) framework which preserves and utilizes locality structures on target data to achieve adaptation without source data. Meanwhile, a label propagation strategy is adopted to improve the discriminability for better adaptation. After LPT, unique samples with insignificant locality structure are identified by an energy-based approach for active annotation. An energy-based pseudo labeling strategy is further applied to generate labels for reliable samples. Finally, with supervision from the annotated samples and pseudo labels, a well adapted model is obtained. Extensive experiments on three widely used UDA benchmarks show that our method is comparable or superior to current state-of-the-art active domain adaptation methods even without access to source data.