We present a new dataset condensation framework termed Squeeze, Recover and
Relabel (SRe$^2$L) that decouples the bilevel optimization of model and
synthetic data during training, to handle varying scales of datasets, model
architectures and image resolutions for effective dataset condensation. The
proposed method demonstrates flexibility across diverse dataset scales and
exhibits multiple advantages in terms of arbitrary resolutions of synthesized
images, low training cost and memory consumption with high-resolution training,
and the ability to scale up to arbitrary evaluation network architectures.
Extensive experiments are conducted on Tiny-ImageNet and full ImageNet-1K
datasets. Under 50 IPC, our approach achieves the highest 42.5% and 60.8%
validation accuracy on Tiny-ImageNet and ImageNet-1K, outperforming all
previous state-of-the-art methods by margins of 14.5% and 32.9%, respectively.
Our approach also outperforms MTT by approximately 52$times$ (ConvNet-4) and
16$times$ (ResNet-18) faster in speed with less memory consumption of
11.6$times$ and 6.4$times$ during data synthesis. Our code and condensed
datasets of 50, 200 IPC with 4K recovery budget are available at
https://zeyuanyin.github.io/projects/SRe2L/.