A Unified Framework for Bidirectional Prototype Learning From Contaminated Faces Across Heterogeneous Domains-Reference-Cited by-同舟云学术

A Unified Framework for Bidirectional Prototype Learning From Contaminated Faces Across Heterogeneous Domains

Author:

Pang Meng¹^ORCID,Wang Binghui²^ORCID,Huang Siyu³,Cheung Yiu-Ming⁴^ORCID,Wen Bihan¹^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

2. Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA

3. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA

4. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

Funder

Ministry of Education, Republic of Singapore, through the Start-Up Grant

National Research Foundation (NRF) Singapore

Singapore Cybersecurity Consortium (SGCSC) Grant Office

NSFC/Research Grants Council (RGC) Joint Research Scheme

RGC General Research Fund

NSFC

Hong Kong Baptist University

Innovation and Technology Fund of Innovation and Technology Commission, Government of the Hong Kong Special Administrative Region

Startup Funding

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

Reference69 articles.

2. Generative adversarial nets;goodfellow;Proc Adv Neural Inf Process Syst (NIPS),2014

Cited by 12 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. Early Diagnosing Parkinson's Disease Via a Deep Learning Model Based on Augmented Facial Expression Data;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

5. Syncretic Space Learning Network for NIR-VIS Face Recognition;ACM Transactions on Multimedia Computing, Communications, and Applications;2023-08-25