ARKG: Adversarially Residual Knowledge Generalization to Open-Set Domain Adaptation

Open-set domain adaptation (OSDA) aims to bridge the gap between labeled source and unlabeled target domains while separating unknown data in the target domain. Recent works have addressed the OSDA setting with notable results, yet they have explored inflexible and limited patterns in latent features. The insufficient generalizability of representation in low-density regions against the large variety of unknown data results in the misclassification of sensitive samples during direct alignment. This study proposes a novel strategy that adversarially leverages residual knowledge generalization (ARKG) at the pixel level to ensure boundary consistency for weighted domain alignment and resilient decision-making. To generate distinctive and generalizable source latent features for alignment with the target domain, a new uncertainty-aware residual space (UARS) is produced using a deep residual network influenced by the target domain. In the framework of integrating the variational autoencoder and generative adversarial network (VAE-GAN), source-like images are generated using diverse sampling from this space. Using the Lipschitz continuous principle, a weighting approach is applied to the target domain to maximize the conditional mutual information (MI) between the shared instances and their latent spaces. Finally, hierarchical decisions at the pixel and feature levels draw a dynamic adversarial boundary between known and unknown data. Extensive results on Office-31, Office-Home, DomainNet, and VisDA datasets show that ARKG achieves superior performance by providing state-of-the-art insights in OSDA. Code is available in https://github.com/ReyhaneGhaffari/ARKG.