Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research.
翻译:传播模型是一种深层次的基因模型,基于两个阶段,即前方扩散阶段和反向扩散阶段。在前方扩散阶段,输入数据通过增加高萨噪音,对几个步骤逐渐感到不适。在反向阶段,一个模型的任务是通过学习逐渐扭转传播过程来恢复原始输入数据,逐步改变传播过程。扩散模型由于所生成样品的质量和多样性而得到广泛赞赏,尽管它们具有已知的计算负担,即由于取样过程中涉及的步骤很多,速度较低。在本调查中,我们全面审查了关于在视觉中应用的传播模型的分解条款,包括在实地的理论和实际贡献。首先,我们确定和提出三个通用传播模型框架,其基础是分辨扩散稳定模型、噪声条件分数模型网络和差异方程式。我们进一步讨论了传播模型与其他深层次的基因模型之间的关系,包括应用的变式自动分析模型和后期分析模型,以及我们所应用的后期模型和后期数据分析模式。首先,我们进一步讨论了一些基于我们系统的传播模型和深层分析模型之间的关系。我们所应用的模型,从可变式分析的模型和后期分析的模型的模型和后期分析性模型。