Sensitivity Analysis for Diffusion Models

• URL: http://arxiv.org/abs/2509.23092v1
• Date: Sat, 27 Sep 2025 03:59:00 GMT
• Title: Sensitivity Analysis for Diffusion Models
• Authors: Christopher Scarvelis, Justin Solomon,
• Abstract summary: Training a diffusion model approximates a map from a data distribution $rho$ to the optimal score function $s_t$ for that distribution.<n>We predict how the score, and ultimately the model's samples, would change under small perturbations to the training set before committing to costly retraining.<n>Our method is robust to numerical and approximation error, and the resulting sensitivities correlate with changes in an image diffusion model's samples after retraining and fine-tuning.
• Score: 12.045369519073525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training a diffusion model approximates a map from a data distribution $\rho$ to the optimal score function $s_t$ for that distribution. Can we differentiate this map? If we could, then we could predict how the score, and ultimately the model's samples, would change under small perturbations to the training set before committing to costly retraining. We give a closed-form procedure for computing this map's directional derivatives, relying only on black-box access to a pre-trained score model and its derivatives with respect to its inputs. We extend this result to estimate the sensitivity of a diffusion model's samples to additive perturbations of its target measure, with runtime comparable to sampling from a diffusion model and computing log-likelihoods along the sample path. Our method is robust to numerical and approximation error, and the resulting sensitivities correlate with changes in an image diffusion model's samples after retraining and fine-tuning.

Related papers

• Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models [30.36841165328262]
  We present a mechanism to steer the sampling diversity of flow matching models.<n>We show that rescaling these allows one to effectively control a local' sampling temperature.
  arXiv  Detail & Related papers  (2025-10-01T17:59:51Z)
• Resolving Memorization in Empirical Diffusion Model for Manifold Data in High-Dimensional Spaces [5.716752583983991]
  When the data distribution consists of n points, empirical diffusion models tend to reproduce existing data points.<n>This work shows that the memorization issue can be solved simply by applying an inertia update at the end of the empirical diffusion simulation.<n>We demonstrate that the distribution of samples from this model approximates the true data distribution on a $C2$ manifold of dimension $d$, within a Wasserstein-1 distance of order $O(n-frac2d+4)$.
  arXiv  Detail & Related papers  (2025-05-05T09:40:41Z)
• DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks [79.50756148780928]
  This paper studies the problem of leveraging pretrained diffusion models for performing discriminative tasks.<n>We extend the discriminative capability of pretrained frozen generative diffusion models from the classification task to the more complex object detection task, by "inverting" a pretrained layout-to-image diffusion model.
  arXiv  Detail & Related papers  (2025-04-24T05:13:27Z)
• A Mixture-Based Framework for Guiding Diffusion Models [19.83064246586143]
  Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems.<n>Recent approaches use pre-trained diffusion models as priors to solve a wide range of such problems.<n>This work proposes a novel mixture approximation of these intermediate distributions.
  arXiv  Detail & Related papers  (2025-02-05T16:26:06Z)
• A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models [14.859580045688487]
  A practical bottleneck of diffusion models is their sampling speed. We propose a novel framework capable of adaptively allocating compute required for the score estimation. We show that our method could significantly improve the sampling throughput of the diffusion models without compromising image quality.
  arXiv  Detail & Related papers  (2024-08-12T05:33:45Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions.<n>We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance.<n>Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models [72.07462371883501]
  We propose emphProjection Regret (PR), an efficient novelty detection method that mitigates the bias of non-semantic information. PR computes the perceptual distance between the test image and its diffusion-based projection to detect abnormality. Extensive experiments demonstrate that PR outperforms the prior art of generative-model-based novelty detection methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-05T09:44:47Z)
• Diffusion models for probabilistic programming [56.47577824219207]
  Diffusion Model Variational Inference (DMVI) is a novel method for automated approximate inference in probabilistic programming languages (PPLs) DMVI is easy to implement, allows hassle-free inference in PPLs without the drawbacks of, e.g., variational inference using normalizing flows, and does not make any constraints on the underlying neural network model.
  arXiv  Detail & Related papers  (2023-11-01T12:17:05Z)
• Generative Diffusion From An Action Principle [0.0]
  We show that score matching can be derived from an action principle, like the ones commonly used in physics. We use this insight to demonstrate the connection between different classes of diffusion models.
  arXiv  Detail & Related papers  (2023-10-06T18:00:00Z)
• Reflected Diffusion Models [93.26107023470979]
  We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data. Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
  arXiv  Detail & Related papers  (2023-04-10T17:54:38Z)
• Score-based Continuous-time Discrete Diffusion Models [102.65769839899315]
  We extend diffusion models to discrete variables by introducing a Markov jump process where the reverse process denoises via a continuous-time Markov chain. We show that an unbiased estimator can be obtained via simple matching the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.
  arXiv  Detail & Related papers  (2022-11-30T05:33:29Z)
• OCD: Learning to Overfit with Conditional Diffusion Models [95.1828574518325]
  We present a dynamic model in which the weights are conditioned on an input sample x. We learn to match those weights that would be obtained by finetuning a base model on x and its label y.
  arXiv  Detail & Related papers  (2022-10-02T09:42:47Z)

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.