Domain adaptation based on hybrid classical-quantum neural network-Reference-Cited by-同舟云学术

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Domain adaptation based on hybrid classical-quantum neural network

Published:2023-06-26 Issue:6 Volume:22 Page:
ISSN:1573-1332
Container-title:Quantum Information Processing
language:en
Short-container-title:Quantum Inf Process

Author:

Wu Yihua,Wu Chunhui,Zhang Anqi,Zhao Shengmei^ORCID

Funder

National Natural Science Foundation of China

Graduate Research and Innovation Projects of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Modeling and Simulation,Signal Processing,Theoretical Computer Science,Statistical and Nonlinear Physics,Electronic, Optical and Magnetic Materials

Link

https://link.springer.com/content/pdf/10.1007/s11128-023-04012-2.pdf

Reference36 articles.

1. Liu, Y., Zhang, W., Wang, J.: Source-free domain adaptation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1215–1224 (2021)

2. Kouw, W.M., Loog, M.: A review of domain adaptation without target labels. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 766–785 (2021)

3. Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)

4. Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: Contrastive adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4893–4902 (2019)

5. Lu, N., Xiao, H., Sun, Y., Han, M., Wang, Y.: A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation. Neurocomputing 427, 96–109 (2021)

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