Open Set Domain Adaptation (OSDA) aims to cope with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for known classes while identifying unknown class samples in the target domain. Most existing OSDA approaches, depending on the final image feature space of deep models, require manually-tuned thresholds, and may easily misclassify unknown samples as known classes. Mixture-of-Experts (MoE) could be a remedy. Within a MoE, different experts handle distinct input features, producing unique expert routing patterns for various classes in a routing feature space. As a result, unknown class samples may display different expert routing patterns to known classes. In this paper, we propose Dual-Space Detection, which exploits the inconsistencies between the image feature space and the routing feature space to detect unknown class samples without any threshold. Graph Router is further introduced to better make use of the spatial information among image patches. Experiments on three different datasets validated the effectiveness and superiority of our approach.

翻译：开放集域适应旨在同时处理源域与目标域之间的分布偏移和标签偏移，在准确分类已知类的同时，识别目标域中的未知类样本。大多数现有开放集域适应方法依赖于深度模型的最终图像特征空间，需要手动调整阈值，且容易将未知样本误分类为已知类。混合专家模型可能提供一种解决方案。在混合专家模型中，不同的专家处理不同的输入特征，在路由特征空间中为不同类别产生独特的专家路由模式。因此，未知类样本可能表现出与已知类不同的专家路由模式。本文提出双空间检测方法，利用图像特征空间与路由特征空间之间的不一致性来检测未知类样本，无需任何阈值。进一步引入图路由器以更好地利用图像块间的空间信息。在三个不同数据集上的实验验证了本方法的有效性和优越性。