SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples

• URL: http://arxiv.org/abs/2512.17051v1
• Date: Thu, 18 Dec 2025 20:37:56 GMT
• Title: SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples
• Abstract summary: In many real-world scenarios, obtaining fully observed samples is expensive or even infeasible.<n>In this work, we study distribution restoration with abundant noisy samples, assuming the corruption process is available as a black-box generator.<n>We show that this task can be framed as a one-sided optimal transport problem and solved via an EM-like algorithm.
• Score: 22.912528721457473
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In many real-world scenarios, obtaining fully observed samples is prohibitively expensive or even infeasible, while partial and noisy observations are comparatively easy to collect. In this work, we study distribution restoration with abundant noisy samples, assuming the corruption process is available as a black-box generator. We show that this task can be framed as a one-sided entropic optimal transport problem and solved via an EM-like algorithm. We further provide a test criterion to determine whether the true underlying distribution is recoverable under per-sample information loss, and show that in otherwise unrecoverable cases, a small number of clean samples can render the distribution largely recoverable. Building on these insights, we introduce SFBD-OMNI, a bridge model-based framework that maps corrupted sample distributions to the ground-truth distribution. Our method generalizes Stochastic Forward-Backward Deconvolution (SFBD; Lu et al., 2025) to handle arbitrary measurement models beyond Gaussian corruption. Experiments across benchmark datasets and diverse measurement settings demonstrate significant improvements in both qualitative and quantitative performance.

Related papers

• Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning [4.341190001906841]
  Wasserstein Filtering (WF) is a novel sample selection framework that discards a fraction of suspicious samples and estimates the target distribution.<n>WF serves as a highly practical, model-agnostic preprocessing tool.<n>It delivers competitive outlier detection performance and provides substantial downstream benefits for generative modeling under heavy contamination.
  arXiv  Detail & Related papers  (2026-08-13T16:12:28Z)
• Uncertainty-based Debiasing and Unlearning for Decontamination [12.593872705071114]
  Uncertainty-Based Decontamination (UBD) estimates per-sample memorization without requiring a contaminated model or knowledge of which samples are contaminated.<n>UBD produces per-sample output distributions substantially closer to those of an uncontaminated model than paraphrasing or choice-permutation baselines.
  arXiv  Detail & Related papers  (2026-06-22T13:26:29Z)
• UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs [21.05820098186702]
  We introduce UnpredictaBench, an evaluation that tests the ability of large language models to capture true underlying distributions.<n>We find that even simple distributional simulation remains challenging, making it a necessary first step toward using LLMs as stand-ins for complex systems.
  arXiv  Detail & Related papers  (2026-06-04T18:18:02Z)
• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• One-shot Conditional Sampling: MMD meets Nearest Neighbors [3.6831672200803993]
  We introduce Conditional Generator using MMD (CGMMD), a novel framework for conditional sampling.<n>A key feature of CGMMD is its ability to produce conditional samples in a single forward pass of the generator.<n>We show that CGMMD performs competitively on synthetic tasks involving complex conditional densities.
  arXiv  Detail & Related papers  (2025-09-29T21:04:50Z)
• Minimax Optimality of the Probability Flow ODE for Diffusion Models [8.15094483029656]
  This work develops the first end-to-end theoretical framework for deterministic ODE-based samplers.<n>We propose a smooth regularized score estimator that simultaneously controls both the $L2$ score error and the associated mean Jacobian error.<n>We demonstrate that the resulting sampler achieves the minimax rate in total variation distance, modulo logarithmic factors.
  arXiv  Detail & Related papers  (2025-03-12T17:51:29Z)
• Self-Guided Generation of Minority Samples Using Diffusion Models [57.319845580050924]
  We present a novel approach for generating minority samples that live on low-density regions of a data manifold. Our framework is built upon diffusion models, leveraging the principle of guided sampling. Experiments on benchmark real datasets demonstrate that our approach can greatly improve the capability of creating realistic low-likelihood minority instances.
  arXiv  Detail & Related papers  (2024-07-16T10:03:29Z)
• Entropy-MCMC: Sampling from Flat Basins with Ease [10.764160559530849]
  We introduce an auxiliary guiding variable, the stationary distribution of which resembles a smoothed posterior free from sharp modes, to lead the MCMC sampler to flat basins. By integrating this guiding variable with the model parameter, we create a simple joint distribution that enables efficient sampling with minimal computational overhead. Empirical results demonstrate that our method can successfully sample from flat basins of the posterior, and outperforms all compared baselines on multiple benchmarks.
  arXiv  Detail & Related papers  (2023-10-09T04:40:20Z)
• Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models [54.182955830194445]
  existing models either fail or face a dramatic drop under the so-called near-distribution" setting. We propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our method improves the near-distribution novelty detection by 6% and passes the state-of-the-art by 1% to 5% across nine novelty detection benchmarks.
  arXiv  Detail & Related papers  (2022-05-28T02:02:53Z)
• Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs [3.491202838583993]
  Energy-Based Models (EBMs) allow for extremely flexible specifications of probability distributions. They do not provide a mechanism for obtaining exact samples from these distributions. We propose a new approximate sampling technique, Quasi Rejection Sampling (QRS), that allows for a trade-off between sampling efficiency and sampling quality.
  arXiv  Detail & Related papers  (2021-12-10T17:51:37Z)
• Learning Energy-Based Models by Diffusion Recovery Likelihood [61.069760183331745]
  We present a diffusion recovery likelihood method to tractably learn and sample from a sequence of energy-based models. After training, synthesized images can be generated by the sampling process that initializes from Gaussian white noise distribution. On unconditional CIFAR-10 our method achieves FID 9.58 and inception score 8.30, superior to the majority of GANs.
  arXiv  Detail & Related papers  (2020-12-15T07:09:02Z)
• One for More: Selecting Generalizable Samples for Generalizable ReID Model [92.40951770273972]
  This paper proposes a one-for-more training objective that takes the generalization ability of selected samples as a loss function. Our proposed one-for-more based sampler can be seamlessly integrated into the ReID training framework.
  arXiv  Detail & Related papers  (2020-12-10T06:37:09Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.