Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate-Reference-Cited by-同舟云学术

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Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Liu Xiaofeng¹,Guo Zhenhua²,Li Site³,Xing Fangxu¹,You Jane⁴,Kuo C.-C. Jay⁵,El Fakhri Georges¹,Woo Jonghye¹

Affiliation:

1. Massachusetts General Hospital and Harvard Medical School,Gorden Center for Medical Imaging,Boston,MA,USA

2. Alibaba Group,Hangzhou,China

3. Carnegie Mellon University,Pittsburgh,PA,USA

4. The Hong Kong Polytechnic University,Dept. of Computing,Hong Kong

5. University of Southern California,Dept. of ECE,Los Angeles,CA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09710205.pdf?arnumber=9710205

Reference66 articles.

1. A unifying view on dataset shift in classification

2. Instance adaptive self-training for unsupervised domain adaptation;mei;ECCV,2020

3. Generative self-training for cross-domain unsupervised tagged-to-cine mri synthesis;liu;MICCAI,2021

4. Feature-Level Frankenstein: Eliminating Variations for Discriminative Recognition

5. Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation

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