Open-Set Domain Generalization Via Metric Learning

In this study, we address open-set domain generalization, which aims to reject unknown class samples while classifying known class samples in unseen domains. Conventional domain generalization has the problem of unknown class samples being classified as known classes because domain generalization methods align feature distributions without distinction between known and unknown classes. To tackle this problem, we propose a decoupling loss that diffuses the feature representations of unknown samples. The loss allows us to construct a feature space that can better distinguish unknown samples. We demonstrate the effectiveness of decoupling loss using open-set domain generalization benchmarks.