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-Expert (MoE) could be a remedy. Within an MoE, different experts address different input features, producing unique expert routing patterns for different classes in a routing feature space. As a result, unknown class samples may also display different expert routing patterns to known classes. This paper proposes 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. The code will come soon.

翻译：开放集域自适应旨在同时应对源域与目标域之间的分布偏移和标签偏移，在实现已知类别准确分类的同时识别目标域中的未知类别样本。现有大多数开放集域自适应方法依赖于深度模型的最终图像特征空间，需要手动调节阈值，且容易将未知类别样本误判为已知类别。混合专家模型可为此提供解决方案：在混合专家模型中，不同专家处理不同的输入特征，在路由特征空间中为不同类别生成独特的专家路由模式。因此，未知类别样本可能展现出与已知类别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，在无需任何阈值的情况下检测未知类别样本。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所提方法的有效性与优越性。代码即将发布。