Abstract
Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled
target domain. Amongst its many variants, open set domain adaptation (OSDA) is perhaps the most challenging one, as it further
assumes the presence of unknown classes in the target domain. In this paper, we study OSDA with a particular focus on enriching its
ability to traverse across larger domain gaps, and we show that existing state-of-the-art methods suffer a considerable performance
drop in the presence of larger domain gaps, especially on a new dataset (PACS) that we re-purposed for OSDA. Exploring this is
pivotal for OSDA as with increasing domain shift, identifying unknown samples in the target domain becomes harder for the model, thus
making negative transfer between source and target domains more challenging. Accordingly, we propose a Mutual-to-Separate (MTS)
framework to address the larger domain gaps. Essentially we design two networks – (a) Sample Separation Network (SSN): which is
trained to learn a hyperplane for separating unknown samples from known ones, and (b) Distribution Matching Network (DMN): which is
trained to maximise domain confusion between source and target domains without unknown samples under the guidance of the SSN.
The key insight lies in how we exploit the mutually beneficial information between these two networks. On closer observation, we see
that SSN can reveal which samples in the target domain belong to the unknown class by instance weighting whereas, DMN pushes apart
the samples that most likely belong to the unknown class in the target domain, which in turn reduces the difficulty of SSN in identifying
unknown samples. It follows that (a) and (b) will mutually supervise each other and alternate until convergence, which can better align the
source and target domains in the shared label space. Extensive experiments on five datasets (Office-31, Office-Home, PACS, VisDA, and
mini DomainNet) demonstrate the efficiency of the proposed method. Detailed ablation experiments also validate the effectiveness of
each component and the generality of the proposed framework. Codes are available at: https://github.com/PRIS-CV/Mutual-to-Separate.