A Survey on Generative Diffusion Models-Reference-Cited by-同舟云学术

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A Survey on Generative Diffusion Models

Published:2024-07 Issue:7 Volume:36 Page:2814-2830
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Cao Hanqun¹^ORCID,Tan Cheng²^ORCID,Gao Zhangyang²^ORCID,Xu Yilun³^ORCID,Chen Guangyong⁴^ORCID,Heng Pheng-Ann¹^ORCID,Li Stan Z.²^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China

2. AI Lab, School of Engineering, Westlake University, Hangzhou, Zhejiang, China

3. Massachusetts Institute of Technology, Cambridge, MA, USA

4. Zhejiang Lab, Hangzhou, Zhejiang, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Hong Kong Special Administrative Region, China

Innovation and Technology Commission

Science and Technology Innovation 2030 - Major Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/69/10549876/10419041.pdf?arnumber=10419041

Reference258 articles.

1. An Introduction to Variational Autoencoders

2. Deep generative models: Survey

3. A tutorial on energy-based learning;LeCun;Predicting Struct. Data,2006

4. Learning deep energy models;Ngiam

5. Generative adversarial networks

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