Domain-Specificity Inducing Transformers for Source-Free Domain
  Adaptation
Sunandini Sanyal Ashish Ramayee Asokan Suvaansh Bhambri Akshay Kulkarni Jogendra Nath Kundu R. Venkatesh Babu
Conventional Domain Adaptation (DA) methods aim to learn domain-invariant
feature representations to improve the target adaptation performance. However,
we motivate that domain-specificity is equally important since in-domain
trained models hold crucial domain-specific properties that are beneficial for
adaptation. Hence, we propose to build a framework that supports
disentanglement and learning of domain-specific factors and task-specific
factors in a unified model. Motivated by the success of vision transformers in
several multi-modal vision problems, we find that queries could be leveraged to
extract the domain-specific factors. Hence, we propose a novel
Domain-specificity-inducing Transformer (DSiT) framework for disentangling and
learning both domain-specific and task-specific factors. To achieve
disentanglement, we propose to construct novel Domain-Representative Inputs
(DRI) with domain-specific information to train a domain classifier with a
novel domain token. We are the first to utilize vision transformers for domain
adaptation in a privacy-oriented source-free setting, and our approach achieves
state-of-the-art performance on single-source, multi-source, and multi-target
benchmarks