Work

Score-Based Image-to-Image Brownian Bridge

Year: 2024

Type: conference-paper

Abstract: Image-to-image translation is defined as the process of learning a mapping between images from a source domain and images from a target domain. The probabilistic structure that maps a fixed initial state to a pinned terminal state through a standard Wiener process is a Brownian bridge. In this paper, we propose a score-based Stochastic Differential Equation (SDE) approach via the Brownian bridges, termed the Amenable Brownian Bridges (A-Bridges), to image-to-image translation tasks as an unconditional diffusion model. Our framework embraces a large family of Brownian bridge models, while the discretization of the linear A-Bridge exploits its advantage that provides the explicit solution in a closed form and thus facilitates the model training. Our model enables the accelerated sampling and has achieved record-breaking performance in sample quality and diversity on benchmark datasets following the guidance of its SDE structure.

Source: ACM International Conference on Multimedia (ACM MM)

DOI: 10.1145/3664647.3680999

Authors: Peiyong Wang, Bohan Xiao, Qisheng He, Carri Glide-Hurst, Ming Dong

Institutions: Wayne State University, University of Wisconsin–Madison

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Heart, Lung, and Blood Institute

Award: G3642456841