REVIEW 11 cited by
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
read the original abstract
Diffusion-based generative models have achieved promising results recently, but raise an array of open questions in terms of conceptual understanding, theoretical analysis, algorithm improvement and extensions to discrete, structured, non-Euclidean domains. This work tries to re-exam the overall framework, in order to gain better theoretical understandings and develop algorithmic extensions for data from arbitrary domains. By viewing diffusion models as latent variable models with unobserved diffusion trajectories and applying maximum likelihood estimation (MLE) with latent trajectories imputed from an auxiliary distribution, we show that both the model construction and the imputation of latent trajectories amount to constructing diffusion bridge processes that achieve deterministic values and constraints at end point, for which we provide a systematic study and a suit of tools. Leveraging our framework, we present 1) a first theoretical error analysis for learning diffusion generation models, and 2) a simple and unified approach to learning on data from different discrete and constrained domains. Experiments show that our methods perform superbly on generating images, semantic segments and 3D point clouds.
Forward citations
Cited by 11 Pith papers
-
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.
-
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.
-
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.
-
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.
-
Scalable Maximum Entropy Reinforcement Learning for Diffusion Policies via Adjoint Matching
Presents adjoint matching for scalable max-ent RL training of diffusion policies, enabling simulation-free optimization.
-
Noise Schedule Design for Diffusion Models: An Optimal Control Perspective
Recasting diffusion noise schedule design as optimal control on Fisher information yields sufficient conditions for O(d/n) sampling error and parametric closed-form schedules that generalize exponential/sigmoid ones a...
-
Conditional Flow Matching for Visually-Guided Acoustic Highlighting
Conditional flow matching with a rollout loss and early audio-visual fusion achieves state-of-the-art results on visually-guided acoustic highlighting.
-
A Gaussian Perspective for Distributional Discrepancy in Generative Diffusion Models
For Gaussian sources, diffusion sampling error has a closed-form KL whose leading term is minimized by a tangent-law noise schedule, and the same KL guides low-NFE time discretization on real images.
-
Rectified Flow: A Marginal Preserving Approach to Optimal Transport
A single-objective rectified flow variant uses neural ODEs trained by regression to monotonically decrease a fixed convex transport cost while preserving marginal distributions.
-
Rectified Schr\"odinger Bridge Matching for Few-Step Visual Navigation
Rectified Schrödinger Bridge Matching uses ε-invariant velocity structure and a learned prior so generative navigation policies reach ~94% cosine similarity and 92% success in three integration steps without distillation.
-
Notes on generative modeling: flow matching, diffusion, optimal transport and Schr{\"o}dinger bridge
Notes recapitulating high-level principles of generative modeling and showing connections between optimal transport, Schrödinger bridge, and flow matching.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.