计算机科学
创造力
生成语法
生成模型
扩散
领域
理解力
人工智能
数据科学
认知科学
心理学
历史
考古
物理
程序设计语言
热力学
社会心理学
作者
Hanqun Cao,Cheng Tan,Zhangyang Gao,Guangyong Chen,Pheng‐Ann Heng,Stan Z. Li
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:43
标识
DOI:10.48550/arxiv.2209.02646
摘要
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 in https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model.
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