Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach
This addresses the challenge of distribution and label shifts in domain adaptation for computer vision, offering a threshold-free method to improve classification accuracy for known classes while identifying unknowns, though it appears incremental as it builds on existing MoE and OSDA frameworks.
The paper tackles the problem of Open Set Domain Adaptation (OSDA) by proposing a Dual-Space Detection approach that uses Mixture-of-Experts to detect unknown class samples without thresholds, achieving validated effectiveness and superiority on three datasets.
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.