Probabilistic diffusion models enjoy increasing popularity in the deep learning community. They generate convincing samples from a learned distribution of input images with a wide field of practical applications. Originally, these approaches were motivated from drift-diffusion processes, but these origins find less attention in recent, practice-oriented publications. We investigate probabilistic diffusion models from the viewpoint of scale-space research and show that they fulfil generalised scale-space properties on evolving probability distributions. Moreover, we discuss similarities and differences between interpretations of the physical core concept of drift-diffusion in the deep learning and model-based world. To this end, we examine relations of probabilistic diffusion to osmosis filters.

翻译：概率扩散模型在深度学习社区中日益流行。它们从学习到的输入图像分布中生成令人信服的样本，具有广泛的实际应用领域。最初，这些方法源自漂移扩散过程，但在近期面向实践的文献中，这些起源受到的关注较少。我们从尺度空间研究的视角探究概率扩散模型，并证明它们在演化概率分布上满足广义尺度空间性质。此外，我们讨论了深度学习和基于模型的世界中对漂移扩散这一物理核心概念解释的异同。为此，我们考察了概率扩散与渗滤滤波器之间的关系。