Learning From Noisy Labels by Regularized Estimation of Annotator Confusion-Reference-Cited by-同舟云学术

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Learning From Noisy Labels by Regularized Estimation of Annotator Confusion

Published:2019-06 Issue: Volume: Page:
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Container-title:2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Tanno Ryutaro,Saeedi Ardavan,Sankaranarayanan Swami,Alexander Daniel C.,Silberman Nathan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8938205/8953184/08953406.pdf?arnumber=8953406

Cited by 136 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. Tackling Noisy Labels With Network Parameter Additive Decomposition;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-09

2. CoFix: Advancing Semi-Supervised Learning with Noisy Label Mitigation Through Sample Selection and Consistent Regularization;International Journal of Pattern Recognition and Artificial Intelligence;2024-08-31

3. Noise‐robust neural networks for medical image segmentation by dual‐strategy sample selection;Concurrency and Computation: Practice and Experience;2024-08-26

4. RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

5. CoMix: Confronting with Noisy Label Learning with Co-training Strategies on Textual Mislabeling;ACM Transactions on Asian and Low-Resource Language Information Processing;2024-08-16

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