Diffusion Probabilistic Fields
AuthorsPeiye Zhuang, Samira Abnar, Jiatao Gu, Alex Schwing, Joshua M. Susskind, Miguel Ángel Bautista
Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under the assumption that data lives in an Euclidean grid. In this paper we introduce Diffusion Probabilistic Fields (DPF), a diffusion model that can learn distributions over continuous functions defined over metric spaces, commonly known as fields. We extend the formulation of diffusion probabilistic models to deal with this field parametrization in an explicit way, enabling us to define and end-to-end learning algorithm that side-steps the requirement of representing fields with latent vectors as in previous approaches. We empirically show that, while using the same denoising network, DPF effectively deals with different modalities like 2D images and 3D geometry, in addition to modeling distributions over fields defined on non-Euclidean metric spaces.
Apple sponsored the International Conference on Learning Representations (ICLR), which was held as a hybrid virtual and in person conference between May 1 - 5 in Kigali, Rwanda. ICLR brings together professionals dedicated to the advancement of deep learning. Below was the schedule of Apple sponsored workshops and events at ICLR 2023.