Diffusion Models as Dataset Distillation Priors

• URL: http://arxiv.org/abs/2510.17421v1
• Date: Mon, 20 Oct 2025 11:04:09 GMT
• Title: Diffusion Models as Dataset Distillation Priors
• Authors: Duo Su, Huyu Wu, Huanran Chen, Yiming Shi, Yuzhu Wang, Xi Ye, Jun Zhu,
• Abstract summary: We propose As Diffusion Priors (DAP), which formalizes representativeness by quantifying the similarity between synthetic and real data in feature space.<n>DAP outperforms state-of-the-art methods in generating high-fidelity datasets.<n>Our work establishes a theoretical connection between diffusion priors and the objectives of dataset distillation.
• Score: 39.4727398182562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dataset distillation aims to synthesize compact yet informative datasets from large ones. A significant challenge in this field is achieving a trifecta of diversity, generalization, and representativeness in a single distilled dataset. Although recent generative dataset distillation methods adopt powerful diffusion models as their foundation models, the inherent representativeness prior in diffusion models is overlooked. Consequently, these approaches often necessitate the integration of external constraints to enhance data quality. To address this, we propose Diffusion As Priors (DAP), which formalizes representativeness by quantifying the similarity between synthetic and real data in feature space using a Mercer kernel. We then introduce this prior as guidance to steer the reverse diffusion process, enhancing the representativeness of distilled samples without any retraining. Extensive experiments on large-scale datasets, such as ImageNet-1K and its subsets, demonstrate that DAP outperforms state-of-the-art methods in generating high-fidelity datasets while achieving superior cross-architecture generalization. Our work not only establishes a theoretical connection between diffusion priors and the objectives of dataset distillation but also provides a practical, training-free framework for improving the quality of the distilled dataset.

Related papers

• Dataset Distillation for Pre-Trained Self-Supervised Vision Models [43.50190223507616]
  dataset distillation aims to find a small set of synthetic images such that training a model on them reproduces the performance of the same model trained on a much larger dataset of real samples.<n>We introduce a method of dataset distillation for this task called Linear Gradient Matching.<n>Our method yields synthetic data that outperform all real-image baselines and, remarkably, generalize across pre-trained vision models.
  arXiv  Detail & Related papers  (2025-11-20T18:59:57Z)
• Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation [19.552569546864913]
  We propose a technique to distill images and their self-supervisedly trained representations into a distilled set.<n>This procedure effectively extracts rich information from real datasets, yielding the distilled sets with enhanced cross-architecture generalizability.<n>In particular, we introduce an innovative parameterization upon images and representations via distinct low-dimensional bases.
  arXiv  Detail & Related papers  (2025-07-29T02:51:56Z)
• Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory [33.38900857290244]
  We present a diversity-driven generative dataset distillation method based on a diffusion model to solve this problem.<n>We introduce self-adaptive memory to align the distribution between distilled and real datasets, assessing the representativeness.<n>Our method outperforms existing state-of-the-art methods in most situations.
  arXiv  Detail & Related papers  (2025-05-26T03:48:56Z)
• Taming Diffusion for Dataset Distillation with High Representativeness [49.3818035378669]
  D3HR is a novel diffusion-based framework to generate distilled datasets with high representativeness.<n>Our experiments demonstrate that D3HR can achieve higher accuracy across different model architectures.
  arXiv  Detail & Related papers  (2025-05-23T22:05:59Z)
• Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation [82.39763984380625]
  We introduce denoising score distillation (DSD), a surprisingly effective and novel approach for training high-quality generative models from low-quality data.<n>DSD pretrains a diffusion model exclusively on noisy, corrupted samples and then distills it into a one-step generator capable of producing refined, clean outputs.
  arXiv  Detail & Related papers  (2025-03-10T17:44:46Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• Importance-Aware Adaptive Dataset Distillation [53.79746115426363]
  Development of deep learning models is enabled by the availability of large-scale datasets. dataset distillation aims to synthesize a compact dataset that retains the essential information from the large original dataset. We propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance.
  arXiv  Detail & Related papers  (2024-01-29T03:29:39Z)

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.