Global–local Bi-alignment for purer unsupervised domain adaptation-Reference-Cited by-同舟云学术

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Global–local Bi-alignment for purer unsupervised domain adaptation

Published:2024-03-28 Issue:10 Volume:80 Page:14925-14952
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Lin Yu-e,Liu Erhu,Liang Xingzhu,Chen Miaomiao,Yan Xinyun

Funder

Research Foundation of the Institute of Environment-friendly Materials and Occupational Health (Wuhu) Anhui University of Science and Technology

Medical Special Cultivation Project of Anhui University of Science and Technology

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06038-4.pdf

Reference71 articles.

1. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

2. Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1492–1500

3. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Process Syst 25:1097–1105

4. Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11976–11986

5. Wang X, Girdhar R, Yu SX, Misra I (2023) Cut and learn for unsupervised object detection and instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 3124–3134

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