Evidential Deep Learning for Open-Set Active Domain Adaptation

Open-set domain adaptation (OSDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain containing novel classes. Traditional OSDA methods rarely account for the uncertainty in predictions and typically require additional training overhead. Evidential deep learning (EDL) transforms the model's predictions from point estimates to distributions over the probability simplex by replacing the standard softmax output of classification neural networks with Dirichlet distributions. Considering the presence of out-of-distribution novel classes in OSDA and the additional overhead of existing methods, we propose EDL for open-set active domain adaptation (EOSADA). Leveraging EDL, we construct an open-set classifier and employ a two-round selection strategy guided by the data uncertainty of target domain samples and semantic similarity scores with known classes. This strategy balances the selection of samples from known and novel classes while identifying informative samples, thereby maximizing the performance of the model in OSDA scenarios without modifying the model structure and utilizing a limited annotation budget. Extensive experiments demonstrate the superiority of our approach.