Multi-Level Augmentation Consistency Learning and Sample Selection for Semi-Supervised Domain Generalization-Reference-Cited by-同舟云学术

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Multi-Level Augmentation Consistency Learning and Sample Selection for Semi-Supervised Domain Generalization

Published:2024-04-14 Issue: Volume:33 Page:6550-6554
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Yu Mei¹,Zhang Yujian²,Li Xuewei¹,Zhang Ruixuan¹,Jiang Han³,Gao Jie¹,Liu Zhiqiang¹

Affiliation:

1. Tianjin University,College of Intelligence and Computing,Tianjin,China,300350

2. Tianjin University,School of Future Technology,Tianjin,China,300350

3. OpenBayes (Tianjin) IT Co., Ltd.,Tianjin,China,300456

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446462.pdf?arnumber=10446462

Reference22 articles.

1. A unifying view on dataset shift in classification

2. Generalizing to Unseen Domains: A Survey on Domain Generalization

3. Semi-Supervised Domain Generalization with Stochastic StyleMatch

4. Fixmatch: Simplifying semi-supervised learning with consistency and confidence;Sohn;Advances in neural information processing systems,2020

5. Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

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