A Survey on Generative Diffusion Models

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hanqun Cao;Cheng Tan;Zhangyang Gao;Yilun Xu;Guangyong Chen;Pheng-Ann Heng;Stan Z. Li
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引用次数: 0

Abstract

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.
生成式扩散模型概览
深度生成模型开启了人类创造力的另一个深邃领域。通过捕捉和归纳数据中的模式,我们进入了全方位人工智能通用创意(AIGC)时代。值得注意的是,扩散模型是公认的最重要的生成模型之一,它将人类的创意具体化为不同领域的有形实例,包括图像、文本、语音、生物和医疗保健。为了提供对扩散的先进而全面的见解,本研究报告从三个不同的角度全面阐释了扩散的发展轨迹和未来方向:扩散的基本表述、算法增强和扩散的多方面应用。对每一层都进行了细致的探讨,以提供对其演变的深刻理解。本文介绍了结构化和总结性的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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