Manifold Diffusion Fields
AuthorsAhmed Elhag, Yuyang Wang, Josh Susskind, Miguel Angel Bautista Martin
AuthorsAhmed Elhag, Yuyang Wang, Josh Susskind, Miguel Angel Bautista Martin
This paper was accepted at the Diffusion Models workshop at NeurIPS 2023.
Score-based models have quickly become the de facto choice for generative modeling of images, text and more recently molecules. However, to adapt a score-based generative modeling to these domains the score network needs to be carefully designed, hampering its applicability to arbitrary data domains. In this paper we tackle this problem by taking a \textit{functional} view of data. This functional view allows to cast seemingly different domains to a common shared representation. We then re-formulate the score function to deal with functional data and show: i) this unified architecture can be effectively applied to different modalities: images, geometry, video, and ii) we can learn generative models of signals defined on non-euclidean geometry.