Adversarial Open-Set Domain Adaptation via Transition Bridge Mechanism

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a source domain with labeled samples to a target domain without labeled samples. UDA assumes that the source and target domains have the same categories, which is often challenging to achieve in real-world scenarios. The target domain usually contains new categories of samples that are not found in the source domain, this setup is called Open-Set Domain Adaptation (OSDA). In OSDA, the abundance of domain-specific features makes learning domain-invariant representations a significant challenge. Existing OSDA methods tend to ignore domain-specific features and directly minimize domain differences, which may lead to unclear boundaries between categories and weaken the generalization ability of the model. To address this problem, the OSDA method based on a Transition Bridge Mechanism (OSTBM) is proposed. Specifically, the OSTBM adds a transition bridging mechanism to the feature extractor and domain discriminator to reduce the interference of domain-specific features in the overall transfer process and improves the discriminative ability of the domain discriminator. This enables better alignment of the source distribution with the known target distribution in the feature alignment process and pushes the unknown target distribution away from the decision boundary. The experimental results show that the proposed method outperforms existing OSDA methods on multiple benchmark datasets, demonstrating its superior performance.