EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation
<|reference_start|>EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation: 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.<|reference_end|>
@article{yang2024empl:,
    title={EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot
  Unsupervised Domain Adaptation},
    author={Wanqi Yang, Haoran Wang, Lei Wang, Ge Song, Yang Gao},
    journal={arXiv preprint arXiv:2407.04066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04066},
    primaryClass={cs.CV}
}