Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation

• URL: http://arxiv.org/abs/2308.14023v1
• Date: Sun, 27 Aug 2023 07:04:51 GMT
• Title: Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
• Authors: Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri, Akshay Kulkarni, Jogendra Nath Kundu, R. Venkatesh Babu
• Abstract summary: We build a framework that supports disentanglement and learning of domain-specific factors and task-specific factors. We are the first to utilize vision transformers for domain adaptation in a privacy-oriented source-free setting.
• Score: 34.533493057674974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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