C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation-Reference-Cited by-同舟云学术

C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Karim Nazmul¹,Mithun Niluthpol Chowdhury²,Rajvanshi Abhinav²,Chiu Han-pang²,Samarasekera Supun²,Rahnavard Nazanin¹

Affiliation:

1. UCF,Department of ECE,Orlando,FL,USA

2. SRI International,Princeton,NJ,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10204943.pdf?arnumber=10204943

Reference93 articles.

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3. Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation;damodaran;Proceedings of the European Conference on Computer Vision (ECCV),0

4. Augmentation consistency-guided self-training for source-free domain adaptive semantic segmentation;prabhu;NeurIPS 2022 Workshop on Distribution Shifts Connecting Methods and Applications,0

5. Confident anchor-induced multi-source free domain adaptation;dong;Advances in neural information processing systems,0

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