论文标题
扩散模型：对方法和应用的全面调查
Diffusion Models: A Comprehensive Survey of Methods and Applications
论文作者
论文摘要
扩散模型已成为一个强大的新家族，其中具有深层生成模型，在许多应用中具有破纪录的性能，包括图像合成，视频生成和分子设计。在这项调查中，我们概述了扩散模型上快速扩展的作品，将研究分为三个关键领域：有效的采样，改善的可能性估计以及使用特殊结构来处理数据。我们还讨论了将扩散模型与其他生成模型相结合以增强结果的潜力。我们进一步回顾了扩散模型在从计算机视觉，自然语言产生，时间数据建模到其他科学学科的跨学科应用的领域中的广泛应用。这项调查旨在对扩散模型的状态提供上下文化的，深入的观察，从而确定关注的关键领域，并指向潜在的进一步探索领域。 github：https：//github.com/yangling0818/diffusion-models-papers-survey-taxonomy。
Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language generation, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy.
