论文标题
逆热耗散的生成建模
Generative Modelling With Inverse Heat Dissipation
论文作者
论文摘要
尽管扩散模型在图像生成中表现出了巨大的成功，但它们的噪声生成过程并未明确考虑图像的结构，例如它们固有的多尺度性质。受到扩散模型的启发和粗到精细建模的经验成功，我们提出了一个新型扩散模型，该模型通过随机逆转热方程来生成图像，这是一种PDE，这是一种PDE，当在图像的2D平面上运行时，可以在局部删除细度信息。我们将带有恒定噪声的正向热方程解释为扩散潜在变量模型中的变异近似。我们的新模型显示了在标准扩散模型中未见的新兴定性特性，例如图像中整体颜色和形状的分离。对自然图像的光谱分析突出了与扩散模型的连接，并揭示了它们中隐式的粗到细节偏见。
While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the empirical success of coarse-to-fine modelling, we propose a new diffusion-like model that generates images through stochastically reversing the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. We interpret the solution of the forward heat equation with constant additive noise as a variational approximation in the diffusion latent variable model. Our new model shows emergent qualitative properties not seen in standard diffusion models, such as disentanglement of overall colour and shape in images. Spectral analysis on natural images highlights connections to diffusion models and reveals an implicit coarse-to-fine inductive bias in them.
