Learning a Maximum Entropy Model for Visual Textures using Diffusion

• URL: http://arxiv.org/abs/2606.17342v1
• Date: Mon, 15 Jun 2026 22:44:10 GMT
• Title: Learning a Maximum Entropy Model for Visual Textures using Diffusion
• Abstract summary: We develop a principled method for unsupervised learning of a set of statistics that are used to constrain a maximum entropy probability model.<n>Despite the compactness of our trained model, it generates texture images whose quality is as good as or better than the current state-of-the-art model.
• Score: 19.229797869081462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual textures -- spatially homogeneous image regions containing repeated elements (e.g. a field of grass, the bark of a tree) -- are ubiquitous in visual scenes and provide important cues for recognizing and analyzing materials and objects. A number of existing texture models extract essential statistics from a single texture image, and can then generate high-quality samples that are visually similar to the original by matching these statistics. However, their statistics are either hand-designed or based on a network pretrained for another purpose (e.g., object recognition). Here, we develop the first principled method for unsupervised learning of a set of statistics that are used to constrain a maximum entropy probability model. We leverage methods developed for generative diffusion models to derive training and sampling procedures, and compare these to the traditional method of sampling via matching the statistics. Despite the compactness of our trained model (512 statistics), it generates texture images whose quality is as good as or better than the current state-of-the-art model (~177k statistics). A more direct comparison of the two models, obtained by synthesizing images that are indistinguishable for one model but maximally different for the other, reveals their relative strengths and weaknesses. Finally, we show that unlike previous statistical texture models, a straight trajectory in the representation space of our model generates homogeneous texture samples that interpolate smoothly between the features of the two end points.

Related papers

• Ambient Diffusion Omni: Training Good Models with Bad Data [45.821861121026394]
  We show how to use low-quality, synthetic, and out-of-distribution images to improve the quality of a diffusion model.<n>We present Ambient Omni, a principled framework to train diffusion models that can extract signal from all available images.
  arXiv  Detail & Related papers  (2025-06-10T22:37:39Z)
• Efficient Flow Matching using Latent Variables [9.363347684114474]
  We show that $texttLatent-CFM$ exhibits improved generation quality with significantly less training and computation than state-of-the-art flow matching models.<n>We also consider generative modeling of spatial fields stemming from physical processes.
  arXiv  Detail & Related papers  (2025-05-07T14:59:23Z)
• DDB: Diffusion Driven Balancing to Address Spurious Correlations [24.940576844328408]
  Deep neural networks trained with Empirical Risk Minimization often fail to generalize to out-of-distribution samples.<n>We propose a Diffusion Driven Balancing (DDB) technique to generate training samples with text-to-image diffusion models.<n>Our experiments show that our technique achieves better worst-group accuracy than the existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-21T15:28:22Z)
• Origin Identification for Text-Guided Image-to-Image Diffusion Models [39.234894330025114]
  We propose origin IDentification for text-guided Image-to-image Diffusion models (ID$2$)<n>A straightforward solution to ID$2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images.<n>To solve this challenge of the proposed ID$2$ task, we contribute the first dataset and a theoretically guaranteed method.
  arXiv  Detail & Related papers  (2025-01-04T20:34:53Z)
• Fast constrained sampling in pre-trained diffusion models [80.99262780028015]
  We propose an algorithm that enables fast, high-quality generation under arbitrary constraints.<n>Our approach produces results that rival or surpass the state-of-the-art training-free inference methods.
  arXiv  Detail & Related papers  (2024-10-24T14:52:38Z)
• Bayesian Inverse Graphics for Few-Shot Concept Learning [3.475273727432576]
  We present a Bayesian model of perception that learns using only minimal data. We show how this representation can be used for downstream tasks such as few-shot classification and estimation.
  arXiv  Detail & Related papers  (2024-09-12T18:30:41Z)
• Provable Statistical Rates for Consistency Diffusion Models [87.28777947976573]
  Despite the state-of-the-art performance, diffusion models are known for their slow sample generation due to the extensive number of steps involved. This paper contributes towards the first statistical theory for consistency models, formulating their training as a distribution discrepancy minimization problem.
  arXiv  Detail & Related papers  (2024-06-23T20:34:18Z)
• A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science [7.2447605934304375]
  We focus on four commonly used notions of statistical distances representing different methodologies. We highlight the intuition behind each distance and explain their merits, scalability, complexity, and pitfalls. We evaluate generative models from different scientific domains, namely a model of decision-making and a model generating medical images.
  arXiv  Detail & Related papers  (2024-03-19T11:16:14Z)
• Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation [53.04220377034574]
  We propose incorporating an analytical image attenuation process into the forward diffusion process for high-quality (un)conditioned image generation.<n>Our method represents the forward image-to-noise mapping as simultaneous textitimage-to-zero mapping and textitzero-to-noise mapping.<n>We have conducted experiments on unconditioned image generation, textite.g., CIFAR-10 and CelebA-HQ-256, and image-conditioned downstream tasks such as super-resolution, saliency detection, edge detection, and image inpainting.
  arXiv  Detail & Related papers  (2023-06-23T18:08:00Z)
• SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
  We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
  arXiv  Detail & Related papers  (2022-11-22T18:00:03Z)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• On Distillation of Guided Diffusion Models [94.95228078141626]
  We propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from. For standard diffusion models trained on the pixelspace, our approach is able to generate images visually comparable to that of the original model. For diffusion models trained on the latent-space (e.g., Stable Diffusion), our approach is able to generate high-fidelity images using as few as 1 to 4 denoising steps.
  arXiv  Detail & Related papers  (2022-10-06T18:03:56Z)
• Sampling from Arbitrary Functions via PSD Models [55.41644538483948]
  We take a two-step approach by first modeling the probability distribution and then sampling from that model. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models.
  arXiv  Detail & Related papers  (2021-10-20T12:25:22Z)

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.