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Particle Denoising Diffusion Sampler

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arxiv 2402.06320 v2 pith:TMPZPBEV submitted 2024-02-09 stat.ML cs.LGstat.CO

Angus Phillips , Hai-Dang Dau , Michael John Hutchinson , Valentin De Bortoli , George Deligiannidis , Arnaud Doucet This is my paper · ORCID
classification stat.MLcs.LGstat.CO
keywords diffusiondenoisingparticledatadistributionherematchingmodels
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Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate samples from the data distribution are then obtained by estimating the time-reversal of this diffusion using score matching ideas. We follow here a similar strategy to sample from unnormalized probability densities and compute their normalizing constants. However, the time-reversed diffusion is here simulated by using an original iterative particle scheme relying on a novel score matching loss. Contrary to standard denoising diffusion models, the resulting Particle Denoising Diffusion Sampler (PDDS) provides asymptotically consistent estimates under mild assumptions. We demonstrate PDDS on multimodal and high dimensional sampling tasks.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bootstrap Flow-Map Tree Sampling Enables Online Feedback Driven Search

    cs.LG 2026-07 conditional novelty 7.0 of 10

    Bootstrap Flow-Map Trees construct complete DDPM-like trajectories with a single NFE and dynamic steps, enabling efficient online feedback-driven search and alignment that beats prior tree and SMC samplers.

  2. FES-FM: Free Energy Surface Sampling via Reduced Flow Matching

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    FES-FM learns a reduced flow-matching transport in collective-variable space to sample free energy surfaces, cutting per-sample generation cost while leaving full-space training cost unchanged.

  3. Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

    cs.CV 2025-01 conditional novelty 6.0 of 10

    The paper provides evidence that Diffusion Posterior Sampling implicitly maximizes a posterior rather than sampling the posterior, and uses this to build faster, better-performing restoration algorithms.

  4. CoDe: Blockwise Control for Denoising Diffusion Models

    cs.CV 2025-02 conditional novelty 5.0 of 10

    CoDe applies blockwise best-of-N sampling during diffusion denoising, with Tweedie-based reward estimates, to align generated images to differentiable or non-differentiable rewards.

  5. Local MAP Sampling for Diffusion Models

    cs.GR 2025-10 conditional novelty 4.0 of 10

    LMAPS frames reverse-diffusion inverse-problem solving as repeated local MAP estimation, unifying existing optimization-based solvers, and achieves strong PSNR gains on tasks like motion deblurring, JPEG restoration, ...

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