Related papers: Domain Adaptation via Prompt Learning

Domain Adaptation via Prompt Learning

URL: http://arxiv.org/abs/2202.06687v1
Date: Mon, 14 Feb 2022 13:25:46 GMT
Title: Domain Adaptation via Prompt Learning
Authors: Chunjiang Ge and Rui Huang and Mixue Xie and Zihang Lai and Shiji Song and Shuang Li and Gao Huang
Abstract summary: Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain. We introduce a novel prompt learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL)
Score: 39.97105851723885
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approaches learn domain-invariant features by aligning source and target feature spaces. Such alignments are imposed by constraints such as statistical discrepancy minimization or adversarial training. However, these constraints could lead to the distortion of semantic feature structures and loss of class discriminability. In this paper, we introduce a novel prompt learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL). In contrast to prior works, our approach makes use of pre-trained vision-language models and optimizes only very few parameters. The main idea is to embed domain information into prompts, a form of representations generated from natural language, which is then used to perform classification. This domain information is shared only by images from the same domain, thereby dynamically adapting the classifier according to each domain. By adopting this paradigm, we show that our model not only outperforms previous methods on several cross-domain benchmarks but also is very efficient to train and easy to implement.

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