论文标题
分析基于扩散的深层生成模型的生成和降解能力
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
论文作者
论文摘要
基于扩散的深层生成模型（DDGM）在生成建模中提供最先进的性能。它们的主要强度来自其独特的设置，在该设置中，训练了模型（向后扩散过程）以扭转正向扩散过程，从而逐渐为输入信号增加了噪声。尽管对DDGM进行了充分的研究，但仍不清楚在向后扩散过程中如何转换少量噪声。在这里，我们专注于分析此问题，以更多地了解DDGM及其脱氧和生成能力的行为。我们观察到一个流体过渡点，该点可以改变向后扩散过程的功能，从生成（损坏的）图像从噪声到将损坏的图像变为最终样本。基于此观察结果，我们假设将DDGM分为两个部分：Deoiser和一个发电机。 DINOISER可以通过Denoising自动编码器进行参数化，而发电机是具有其自身参数集的基于扩散的模型。我们在实验中验证了我们的主张，表明了其利弊。
Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art performance in generative modeling. Their main strength comes from their unique setup in which a model (the backward diffusion process) is trained to reverse the forward diffusion process, which gradually adds noise to the input signal. Although DDGMs are well studied, it is still unclear how the small amount of noise is transformed during the backward diffusion process. Here, we focus on analyzing this problem to gain more insight into the behavior of DDGMs and their denoising and generative capabilities. We observe a fluid transition point that changes the functionality of the backward diffusion process from generating a (corrupted) image from noise to denoising the corrupted image to the final sample. Based on this observation, we postulate to divide a DDGM into two parts: a denoiser and a generator. The denoiser could be parameterized by a denoising auto-encoder, while the generator is a diffusion-based model with its own set of parameters. We experimentally validate our proposition, showing its pros and cons.
