MGDD: A Meta Generator for Fast Dataset Distillation

In one paragraph

Produces synthetic images from a generator network conditioned on a dataset-distillation initialization, with synthetic labels solved in closed form via least squares in feature space, and meta-trains the generator across many datasets so that adapting to a new target needs only a few steps; reports parity with state-of-the-art distillation baselines at 22x less computation and strong generalization to synthetic-set sizes unseen during adaptation.