MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation

Author:

Wang Fan1,Han Zhongyi1,Zhang Zhiyan1,He Rundong1,Yin Yilong1

Affiliation:

1. School of Software, Shandong University,China

Funder

National Natural Science Foundation of China

CAST

Publisher

IEEE

Reference55 articles.

1. Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation

2. Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data;huang;Advances in neural information processing systems,2021

3. Attract, perturb, and explore: Learning a feature alignment network for semi-supervised domain adaptation;kim;European Conference on Computer Vision,0

4. Multi-class active learning for image classification

5. Exploiting the intrinsic neighborhood structure for source-free domain adaptation;yang;Advances in Neural Infor-Mation Processing Systems,2021

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Active domain adaptation with mining diverse knowledge: An updated class consensus dictionary approach;Information Sciences;2024-05

2. You only label once: A self‐adaptive clustering‐based method for source‐free active domain adaptation;IET Image Processing;2024-01-05

3. Unsupervised Camouflaged Object Segmentation as Domain Adaptation;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

4. Local Context-Aware Active Domain Adaptation;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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