MaxMatch: Semi-Supervised Learning With Worst-Case Consistency-Reference-Cited by-同舟云学术

MaxMatch: Semi-Supervised Learning With Worst-Case Consistency

Author:

Jiang Yangbangyan¹^ORCID,Li Xiaodan²,Chen Yuefeng²,He Yuan²^ORCID,Xu Qianqian³^ORCID,Yang Zhiyong⁴^ORCID,Cao Xiaochun⁵^ORCID,Huang Qingming⁴^ORCID

Affiliation:

1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

2. Security Department of Alibaba Group, Hangzhou, China

3. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

4. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

5. School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference65 articles.

1. Temporal ensembling for semi-supervised learning;laine;Proc Int Conf Learn Representations,2017

3. ReMixMatch: Semi-supervised learning with distribution matching and augmentation anchoring;berthelot;Proc Int Conf Learn Representations,2020

4. An analysis of single-layer networks in unsupervised feature learning;coates;Proc Int Conf Artif Intell Statist,2011

5. Adversarially robust generalization just requires more unlabeled data;zhai,2019

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