# Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation Source: [Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:21:39.376Z Authors: Yanda Chen, Gongwei Chen, Miao Zhang, Weili Guan, Liqiang Nie Published: 2025-03-24T16:47:40.000Z ## Abstract Dataset distillation (DD) excels in synthesizing a small number of images per class (IPC) but struggles to maintain its effectiveness in high-IPC settings. Recent works on dataset distillation demonstrate that combining distilled and real data can mitigate the effectiveness decay. However, our analysis of the combination paradigm reveals that the current one-shot and independent selection mechanism induces an incompatibility issue between distilled and real images. To address this issue, we introduce a novel curriculum coarse-to-fine selection (CCFS) method for efficient high-IPC dataset distillation. CCFS employs a curriculum selection framework for real data selection, where we leverage a coarse-to-fine strategy to select appropriate real data based on the current synthetic dataset in each curriculum. Extensive experiments validate CCFS, surpassing the state-of-the-art by +6.6\\% on CIFAR-10, +5.8\\% on CIFAR-100, and +3.4\\% on Tiny-ImageNet under high-IPC settings. Notably, CCFS achieves 60.2\\% test accuracy on ResNet-18 with a 20\\% compression ratio of Tiny-ImageNet, closely matching full-dataset training with only 0.3\\% degradation. Code: https://github.com/CYDaaa30/CCFS. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()