Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning-Reference-Cited by-同舟云学术

Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning

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

Author:

Li Ming¹,Li Qingli¹,Wang Yan¹

Affiliation:

1. East China Normal University,Shanghai Key Laboratory of Multidimensional Information Processing

Funder

National Natural Science Foundation of China

Shanghai Natural Science Foundation

Science and Technology Commission of Shanghai Municipality

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203348.pdf?arnumber=10203348

Reference37 articles.

1. Transformation consistent self-ensembling model for semi-supervised medical image segmentation;li;IEEE Trans Neural Networks and Learning Systems,2020

2. Distribution alignment: A unified frame-work for long-tail visual recognition;zhang;Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,0

3. Federated optimization in heterogeneous networks;li;Proceedings of Machine Learning and Systems,0

4. Flexmatch: Boosting semi-supervised learning with curricu-lum pseudo labeling;zhang;Advances in Neural Information Pro-cessing Systems,2021

5. Semifed: Semi-supervised federated learning with consistency and pseudo-labeling;lin;ar Xiv preprint,2021

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive survey of federated transfer learning: challenges, methods and applications;Frontiers of Computer Science;2024-07-23

2. Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. GFD-SSL: generative federated knowledge distillation-based semi-supervised learning;International Journal of Machine Learning and Cybernetics;2024-06-23

4. Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

5. Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation;IEEE Transactions on Medical Imaging;2024-05