Continual Learning of Generative Models With Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence-Reference-Cited by-同舟云学术

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Continual Learning of Generative Models With Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence

Published:2023 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Dedeoglu Mehmet¹^ORCID,Lin Sen²^ORCID,Zhang Zhaofeng¹^ORCID,Zhang Junshan³^ORCID

Affiliation:

1. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

2. AI-EDGE Institute, The Ohio State University, Columbus, OH, USA

3. Department of Electrical and Computer Engineering, University of California at Davis, Davis, CA, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10070745.pdf?arnumber=10070745

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