Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity-Reference-Cited by-同舟云学术

Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity

Author:

Feng Lei¹^ORCID,Shu Senlin²,Cao Yuzhou¹,Tao Lue³,Wei Hongxin¹^ORCID,Xiang Tao²^ORCID,An Bo¹^ORCID,Niu Gang⁴^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanyang Technological University, Singapore

2. College of Computer Science, Chongqing University, Chongqing, China

3. National Key Lab for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China

4. RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

Funder

National Key R&D Program of China

National Research Foundation Singapore

National Natural Science Foundation of China

Chongqing Overseas Chinese Entrepreneurship and Innovation Support Program

CAAI-Huawei MindSpore Open Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

Reference63 articles.

1. PiCO: Contrastive label disambiguation for partial label learning;wang;Proc Int Conf Learn Representations,2022

3. Provably consistent partial-label learning;feng;Proc Int Conf Neural Inf Process,2020

4. Rademacher and gaussian complexities: Risk bounds and structural results;bartlett;J Mach Learn Res,2002