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:概率扩散模型在深层学习界越来越受欢迎,它们从广泛实际应用领域的素材图像传播中产生令人信服的样本,最初,这些方法来自漂移扩散过程,但这些来源在近期面向实践的出版物中受到的注意较少。我们从空间规模研究的角度对概率扩散模型进行调查,并表明这些模型在变化中的概率分布上达到了一般的尺度空间特性。此外,我们讨论了深层学习和模型世界对漂移扩散物理核心概念的诠释之间的相似性和差异。为此,我们研究了向渗透过滤器传播概率之间的关系。</s>