提出几何感知数据压缩方法，提升扩散模型训练效果。
Geometry-Aware Dataset Condensation for Diffusion Model Training
- 将真实子集选择重构为几何对齐问题，保留数据分布结构。
- 在不同规模和分辨率下均实现更优的生成质量和分布覆盖。
- 适合需要高效训练且保持真实性的扩散模型研究者使用。
数据压缩旨在通过合成或选择从真实数据中构建紧凑数据集。然而，现有方法不适用于扩散模型训练：合成数据常生成低保真度样本，难以满足真实建模需求；而真实子集选择通常无法保留扩散似然目标所需的分布几何结构。为此，我们提出将真实子集选择重新定义为几何感知的分布对齐问题。通过引入单边部分最优传输，该方法在保持全数据分布对齐的同时，允许低密度区域存在未匹配质量，从而确保扩散模型训练所需的几何结构被有效保留。为进一步保障分布保真度，还结合轻量级特征统计与语义一致性正则化。提出一种高效的两阶段离散优化策略以实现此目标。在多种扩散模型、子集规模、图像分辨率及训练轮次下的大量实验表明，本方法在扩散模型训练中实现了更高的保真度与分布覆盖性。代码已开源：https://github.com/2018cx/GADC。
原文摘要 · Abstract (English)
Dataset condensation aims to construct compact datasets from real data via synthesis or selection. However, existing approaches are ill-suited for diffusion model training: synthetic data generation often yields low-fidelity samples unsuitable for authentic modeling, while real subset selection typically fails to preserve the distributional geometry required by diffusion likelihood objectives. To address this, we propose to reformulate real subset selection as a geometry-aware distribution alignment problem. By incorporating one-sided partial optimal transport, our method selectively aligns a compact subset with the full data distribution while allowing unmatched mass in low-density regions, ensuring the preserved geometric structure necessary for effective diffusion model training. To further ensure distributional fidelity, we complement geometric alignment with lightweight feature-statistics and semantic consistency regularization. An efficient two-stage discrete optimization strategy is proposed to achieve this alignment objective. Extensive experiments across diffusion variants, subset sizes, image resolutions, and training rounds show that our method achieves superior fidelity and distributional coverage in diffusion model training. Codes are available at https://github.com/2018cx/GADC.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。