While deep learning techniques have proven successful in image-related tasks, the exponentially increased data storage and computation costs become a significant challenge. Dataset distillation addresses these challenges by synthesizing only a few images for each class that encapsulate all essential information. Most current methods focus on matching. The problems lie in the synthetic images not being human-readable and the dataset performance being insufficient for downstream learning tasks. Moreover, the distillation time can quickly get out of bounds when the number of synthetic images per class increases even slightly. To address this, we train a class conditional latent diffusion model capable of generating realistic synthetic images with labels. The sampling time can be reduced to several tens of images per seconds. We demonstrate that models can be effectively trained using only a small set of synthetic images and evaluated on a large real test set. Our approach achieved rank \(1\) in The First Dataset Distillation Challenge at ECCV 2024 on the CIFAR100 and TinyImageNet datasets.

本研究解决了深度学习在图像任务中面临的数据存储和计算成本过高的问题。通过训练一个类条件潜在扩散模型，生成可读的合成图像，显著提升了数据集性能并减少了蒸馏时间。该方法在ECCV 2024的首个数据集蒸馏挑战中，CIFAR100和TinyImageNet数据集上取得了第一名的佳绩。

数据高效生成用于数据集蒸馏