Dataset distillation is an emerging dataset reduction method, which condenses
large-scale datasets while maintaining task accuracy. Current methods have
integrated parameterization techniques to boost synthetic dataset performance
by shifting the optimization space from pixel to another informative feature
domain. However, they limit themselves to a fixed optimization space for
distillation, neglecting the diverse guidance across different informative
latent spaces. To overcome this limitation, we propose a novel parameterization
method dubbed Hierarchical Generative Latent Distillation (H-GLaD), to
systematically explore hierarchical layers within the generative adversarial
networks (GANs). This allows us to progressively span from the initial latent
space to the final pixel space. In addition, we introduce a novel
class-relevant feature distance metric to alleviate the computational burden
associated with synthetic dataset evaluation, bridging the gap between
synthetic and original datasets. Experimental results demonstrate that the
proposed H-GLaD achieves a significant improvement in both same-architecture
and cross-architecture performance with equivalent time consumption.

通过在生成对抗网络中逐渐跨越初始潜在空间到最终像素空间，以及引入一个新的与类别相关的特征距离度量方法，我们提出了一种名为层次生成潜在蒸馏（H-GLaD）的新型参数化方法，用于在不同层次的层中系统地探索。实验证明，所提出的 H-GLaD 在相同架构和不同架构性能上显著改进，且时间开销相等。