标题： 带有逆重正化群流的生成扩散模型 显示英文标题

标题： Generative diffusion model with inverse renormalization group flows

摘要： 扩散模型是一类生成模型，通过去除由白噪声破坏的样本中的噪声来生成数据。 尽管扩散模型在计算机视觉、音频合成和点云生成中取得了成功，但到目前为止，它们忽略了数据中的固有多尺度结构，并且由于需要许多迭代步骤而导致生成过程缓慢。 在物理学中，重整化群提供了一个基本框架，用于连接不同尺度并给出精确的粗粒化模型。 在这里，我们引入了一种基于重整化群的扩散模型，利用数据分布的多尺度特性以实现高质量的数据生成。 秉承重整化群过程的精神，我们定义了一个流方程，该方程逐步从细尺度细节到粗粒化结构中消除数据信息。 通过逆转重整化群流，我们的模型能够以从粗到细的方式生成高质量样本。 我们通过在蛋白质结构预测和图像生成中的应用验证了该模型的通用性。 我们的模型在标准评估指标上始终优于传统扩散模型，样本质量和/或采样速度提高了数量级。 所提出的方法减轻了生成扩散模型中对数据相关超参数调优的需求，显示出基于重整化群概念系统提高样本效率的前景。 显示英文摘要

摘要： Diffusion models represent a class of generative models that produce data by denoising a sample corrupted by white noise. Despite the success of diffusion models in computer vision, audio synthesis, and point cloud generation, so far they overlook inherent multiscale structures in data and have a slow generation process due to many iteration steps. In physics, the renormalization group offers a fundamental framework for linking different scales and giving an accurate coarse-grained model. Here we introduce a renormalization group-based diffusion model that leverages multiscale nature of data distributions for realizing a high-quality data generation. In the spirit of renormalization group procedures, we define a flow equation that progressively erases data information from fine-scale details to coarse-grained structures. Through reversing the renormalization group flows, our model is able to generate high-quality samples in a coarse-to-fine manner. We validate the versatility of the model through applications to protein structure prediction and image generation. Our model consistently outperforms conventional diffusion models across standard evaluation metrics, enhancing sample quality and/or accelerating sampling speed by an order of magnitude. The proposed method alleviates the need for data-dependent tuning of hyperparameters in the generative diffusion models, showing promise for systematically increasing sample efficiency based on the concept of the renormalization group.