EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2407.04066v1
• Date: Thu, 4 Jul 2024 17:13:06 GMT
• Title: EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation
• Authors: Wanqi Yang, Haoran Wang, Lei Wang, Ge Song, Yang Gao,
• Abstract summary: We propose a novel Efficient Meta Prompt Learning Framework for FS-UDA. Within this framework, we use pre-trained CLIP model as the feature learning base model. Our method has the large improvement of at least 15.4% on 5-way 1-shot and 8.7% on 5-way 5-shot, compared with the state-of-the-art methods.
• Score: 22.586094394391747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot unsupervised domain adaptation (FS-UDA) utilizes few-shot labeled source domain data to realize effective classification in unlabeled target domain. However, current FS-UDA methods are still suffer from two issues: 1) the data from different domains can not be effectively aligned by few-shot labeled data due to the large domain gaps, 2) it is unstable and time-consuming to generalize to new FS-UDA tasks.To address this issue, we put forward a novel Efficient Meta Prompt Learning Framework for FS-UDA. Within this framework, we use pre-trained CLIP model as the feature learning base model. First, we design domain-shared prompt learning vectors composed of virtual tokens, which mainly learns the meta knowledge from a large number of meta tasks to mitigate domain gaps. Secondly, we also design a task-shared prompt learning network to adaptively learn specific prompt vectors for each task, which aims to realize fast adaptation and task generalization. Thirdly, we learn a task-specific cross-domain alignment projection and a task-specific classifier with closed-form solutions for each meta task, which can efficiently adapt the model to new tasks in one step. The whole learning process is formulated as a bilevel optimization problem, and a good initialization of model parameters is learned through meta-learning. Extensive experimental study demonstrates the promising performance of our framework on benchmark datasets. Our method has the large improvement of at least 15.4% on 5-way 1-shot and 8.7% on 5-way 5-shot, compared with the state-of-the-art methods. Also, the performance of our method on all the test tasks is more stable than the other methods.

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