Cluster-based Adversarial Decision Boundary for domain-adaptive open set recognition

Domain adaptation has achieved significant progress recently by adapting models trained on a source domain to an unlabeled target domain. Open Set Domain adaptation (OSDA) has drawn much attention nowadays, where the target domain contains some exclusive categories other than the source domain's known classes. With no label in the target data, existing OSDA methods often suffer from negative transfer. Conventional methods for unknown class rejection require an empirical setting of the confidence threshold, which lacks flexibility since the model confidence may vary during the training process, and our motivation is to omit the effort of setting the rejection threshold manually. Based on the idea that latent features of the same class should be in the same cluster to address this issue, we propose a domain adaptive open set recognition framework: Cluster-based Adversarial Decision Boundary (CADB). Specifically, we design an end-to-end unknown class rejection model consisting of three components: known class prototype estimation under the cluster assumption; known class similarity score estimation; and adaptive unknown class rejection threshold generation with adversarial feature suppression. These three components work as one entity to give a similarity score for each sample. Those samples that are less similar to the cluster prototype compared with the counterfactual features are rejected as the unknown class. Extensive evaluations are conducted to verify the effectiveness and robustness of the proposed boundary generation procedure.