Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space
  Detection Approach
Zhenbang Du Jiayu An Jiahao Hong Dongrui Wu
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.