Positive-Unlabeled Learning With Label Distribution Alignment-Reference-Cited by-同舟云学术

Positive-Unlabeled Learning With Label Distribution Alignment

Author:

Jiang Yangbangyan¹^ORCID,Xu Qianqian²^ORCID,Zhao Yunrui¹^ORCID,Yang Zhiyong¹^ORCID,Wen Peisong²^ORCID,Cao Xiaochun³^ORCID,Huang Qingming¹^ORCID

Affiliation:

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

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

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

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Youth Innovation Promotion Association of the Chinese Academy of Sciences

Strategic Priority Research Program of Chinese Academy of Sciences

Innovation Funding of ICT, CAS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

Reference66 articles.

2. MixPUL: Consistency-based augmentation for positive and unlabeled learning;wei,2020

3. Learning from positive and unlabeled data with a selection bias;kato;Proc Int Conf Learn Representations,2019

4. Mixup: Beyond empirical risk minimization;zhang;Proc Int Conf Learn Representations,2018

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