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Score-Based Image-to-Image Brownian Bridge.

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Summary
This summary is machine-generated.

This study introduces Amenable Brownian Bridges (A-Bridges), a novel diffusion model for image-to-image translation using score-based Stochastic Differential Equations. A-Bridges achieve superior sample quality and diversity with accelerated sampling.

Keywords:
A-BridgesBrownian BridgeDiffusion ModelsGenerative ModelsImage-to-Image TranslationScore-Based ModelsStochastic Differential Equations (SDE)Unconditional Diffusion Process

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Image-to-image translation involves learning mappings between source and target image domains.
  • Brownian bridges offer a probabilistic framework for mapping initial to terminal states via Wiener processes.

Purpose of the Study:

  • To introduce a novel score-based Stochastic Differential Equation (SDE) approach for image-to-image translation.
  • To leverage Brownian bridges for unconditional diffusion models, termed Amenable Brownian Bridges (A-Bridges).

Main Methods:

  • Utilizing a large family of Brownian bridge models within the A-Bridges framework.
  • Discretizing the linear A-Bridge to exploit its closed-form explicit solution for efficient model training.
  • Employing the SDE structure for accelerated sampling.

Main Results:

  • Achieved record-breaking performance in sample quality and diversity on benchmark datasets.
  • Demonstrated accelerated sampling capabilities.
  • Facilitated model training through the closed-form solution of the linear A-Bridge.

Conclusions:

  • A-Bridges represent a powerful new unconditional diffusion model for image-to-image translation.
  • The proposed framework enhances both the quality and diversity of generated images.
  • The SDE-guided approach enables efficient and high-performance image translation.