Efficient Dataset Distillation via Minimax Diffusion

arXiv:2311.15529v2 Announce Type: replace
Abstract: Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the original large-scale one. However, previous distillation methods heavily rely on the sample-wise iterative optimization scheme. As the images-per-class (IPC) setting or image resolution grows larger, the necessary computation will demand overwhelming time and resources. In this work, we intend to incorporate generative diffusion techniques for computing the surrogate dataset. Observing that …

arxiv cs.cv dataset diffusion distillation minimax type via