Enhancing Semi-Supervised Learning with Cross-Modal Knowledge-Reference-Cited by-同舟云学术

Enhancing Semi-Supervised Learning with Cross-Modal Knowledge

Published:2022-10-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th ACM International Conference on Multimedia
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Author:

Zhu Hui¹,Lu Yongchun²,Wang Hongbin²,Zhou Xunyi²,Ma Qin³,Liu Yanhong²,Jiang Ning²,Wei Xin²,Zeng Linchengxi²,Zhao Xiaofang⁴

Affiliation:

1. Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Beijing, China

2. Mashang Consumer Finance Co., Ltd., Beijing, China

3. China Agricultural University, Beijing, China

4. Institute of Computing Technology & Institute of Intelligent Computing Technology, Suzhou, Chinese Academy of Sciences, Beijing, China

Funder

the National Key Research Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3503161.3548026

Reference43 articles.

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2. Curriculum learning

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4. MixMatch: A Holistic Approach to Semi-Supervised Learning. In Advances in Neural Information Processing Systems;Berthelot David;NeurIPS,2019

5. Lucas Beyer , Xiaohua Zhai , Avital Oliver , and Alexander Kolesnikov . 2019 . S4L: Self-Supervised Semi-Supervised Learning . In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019 . 1476--1485. https://doi.org/10.1109/ICCV2019.00156 Lucas Beyer, Xiaohua Zhai, Avital Oliver, and Alexander Kolesnikov. 2019. S4L: Self-Supervised Semi-Supervised Learning. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019. 1476--1485. https://doi.org/10.1109/ICCV2019.00156

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