This repository implements a dataset distillation method published at CVPR 2022. The approach learns a small set of synthetic images such that models trained exclusively on them achieve similar test performance to models trained on the full real dataset. It works by optimizing synthetic data to induce similar training dynamics in student networks as expert networks trained on real data, measuring error in parameter space across training iterations.