Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
Let us build bridges: Understanding and extending diffusion generative models
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
ABC enables any-subset autoregressive generation of continuous stochastic processes via non-Markovian diffusion bridges that track physical time and allow path-dependent conditioning.
RSBM exploits velocity field invariance across regularization levels to achieve over 94% cosine similarity and 92% success in visual navigation using only 3 integration steps.
Stochastic interpolants unify flow-based and diffusion-based generative models by bridging target densities exactly via latent-variable processes whose drifts minimize quadratic objectives.
citing papers explorer
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
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ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
ABC enables any-subset autoregressive generation of continuous stochastic processes via non-Markovian diffusion bridges that track physical time and allow path-dependent conditioning.
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Rectified Schr\"odinger Bridge Matching for Few-Step Visual Navigation
RSBM exploits velocity field invariance across regularization levels to achieve over 94% cosine similarity and 92% success in visual navigation using only 3 integration steps.
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Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Stochastic interpolants unify flow-based and diffusion-based generative models by bridging target densities exactly via latent-variable processes whose drifts minimize quadratic objectives.