Dataset Distillation via Adversarial Prediction Matching
Mingyang Chen Bo Huang Junda Lu Bing Li Yi Wang Minhao Cheng Wei Wang
Dataset distillation is the technique of synthesizing smaller condensed
datasets from large original datasets while retaining necessary information to
persist the effect. In this paper, we approach the dataset distillation problem
from a novel perspective: we regard minimizing the prediction discrepancy on
the real data distribution between models, which are respectively trained on
the large original dataset and on the small distilled dataset, as a conduit for
condensing information from the raw data into the distilled version. An
adversarial framework is proposed to solve the problem efficiently. In contrast
to existing distillation methods involving nested optimization or long-range
gradient unrolling, our approach hinges on single-level optimization. This
ensures the memory efficiency of our method and provides a flexible tradeoff
between time and memory budgets, allowing us to distil ImageNet-1K using a
minimum of only 6.5GB of GPU memory. Under the optimal tradeoff strategy, it
requires only 2.5$\times$ less memory and 5$\times$ less runtime compared to
the state-of-the-art. Empirically, our method can produce synthetic datasets
just 10% the size of the original, yet achieve, on average, 94% of the test
accuracy of models trained on the full original datasets including ImageNet-1K,
significantly surpassing state-of-the-art. Additionally, extensive tests reveal
that our distilled datasets excel in cross-architecture generalization
capabilities.