Dataset Distillation in Large Data Era
Abstract: Dataset distillation or condensation aims to generate a smaller but representative subset from a large dataset, which allows a model to be trained more efficiently, meanwhile evaluating on the original testing data distribution to achieve decent performance. Previous decoupled methods like SRe$^2$L simply use a unified gradient update scheme for synthesizing data from Gaussian noise, while, we notice that the initial several update iterations will determine the final outline of synthesis, thus an improper gradient update strategy may dramatically affect the final generation quality. To address this, we introduce a simple yet effective global-to-local gradient refinement approach enabled by curriculum data augmentation ($\texttt{CDA}$) during data synthesis. The proposed framework achieves the current published highest accuracy on both large-scale ImageNet-1K and 21K with 63.2% under IPC (Images Per Class) 50 and 36.1% under IPC 20, using a regular input resolution of 224$\times$224. The proposed model outperforms the current state-of-the-art methods like SRe$^2$L, TESLA, and MTT by more than 4% Top-1 accuracy on ImageNet-1K/21K and for the first time, reduces the gap to its full-data training counterparts to less than absolute 15%. Moreover, this work represents the inaugural success in dataset distillation on the larger-scale ImageNet-21K dataset under the standard 224$\times$224 resolution. Our distilled ImageNet-21K dataset of 20 IPC, 2K recovery budget is available anonymously at https://drive.google.com/file/d/13j92BUEGKNnyvLaGEcUMqMWKX08428vZ/view?usp=sharing.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Yu-Xiong_Wang1
Submission Number: 2872
Loading