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
this https URL

SRe$^2$L 是一种新的数据集压缩框架，可以在训练期间解耦模型和合成数据的双层优化，以处理各种规模的数据集、模型结构和图像分辨率，具有高分辨率训练、低训练成本和内存消耗，以及在任意评估网络结构下扩展的能力。