Abstract
In this paper, we address the problem of generative dataset distillation that utilizes generative models to synthesize images. The generator may produce any number of images under a preserved evaluation time. In this work, we leverage the popular diffusion model as the generator to compute a surrogate dataset, boosted by a min-max loss to control the dataset's diversity and representativeness during training. However, the diffusion model is time-consuming when generating images, as it requires an iterative generation process. We observe a critical trade-off between the number of image samples and the image quality controlled by the diffusion steps and propose Diffusion Step Reduction to achieve optimal performance. This paper details our comprehensive method and its performance. Our model achieved $2^{nd}$ place in the generative track of \href{this https URL}{The First Dataset Distillation Challenge of ECCV2024}, demonstrating its superior performance.
Abstract (translated)
在这篇论文中，我们解决了利用生成模型合成图像的生成数据集提炼问题。生成器可以在保持评估时间不变的情况下产生任意数量的图像。在此研究中，我们采用流行的扩散模型作为生成器来计算一个替代数据集，并通过最小-最大损失函数在训练过程中控制数据集的多样性和代表性。然而，当生成图像时，扩散模型会非常耗时，因为它需要进行迭代生成过程。我们观察到，在由扩散步骤控制的图像质量和样本数量之间存在关键的权衡关系，并提出减少扩散步数的方法以达到最佳性能。本文详细介绍了我们的全面方法及其表现情况。在欧洲计算机视觉会议（ECCV2024）首次数据集提炼挑战赛的生成赛道中，我们的模型获得了第二名的好成绩，展示了其优越的表现能力。
URL
https://arxiv.org/abs/2503.18626
