A Survey on Generative Diffusion Models

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount gene...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 36; no. 7; pp. 2814 - 2830
Main Authors: Cao, Hanqun, Tan, Cheng, Gao, Zhangyang, Xu, Yilun, Chen, Guangyong, Heng, Pheng-Ann, Li, Stan Z.
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial intelligence
Computational modeling
Creativity
deep generative model
diffusion algorithm
diffusion applications
Diffusion layers
Diffusion model
Kernel
Markov processes
Mathematical models
Noise reduction
Surveys
Training
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, and healthcare. To provide advanced and comprehensive insights into diffusion, this survey comprehensively elucidates its developmental trajectory and future directions from three distinct angles: the fundamental formulation of diffusion, algorithmic enhancements, and the manifold applications of diffusion. Each layer is meticulously explored to offer a profound comprehension of its evolution. Structured and summarized approaches are presented here.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2024.3361474