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arXiv · 2302.06755

Dataset Distillation with Convexified Implicit Gradients

Noel Loo·Ramin Hasani·Mathias Lechner·Daniela Rus
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Abstract

We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this end, we first formulate dataset distillation as a bi-level optimization problem. Then, we show how implicit gradients can be effectively used to compute meta-gradient updates. We further equip the algorithm with a convexified approximation that corresponds to learning on top of a frozen finite-width neural tangent kernel. Finally, we improve bias in implicit gradients by parameterizing the neural network to enable analytical computation of final-layer parameters given the body parameters. RCIG establishes the new state-of-the-art on a diverse series of dataset distillation tasks. Notably, with one image per class, on resized ImageNet, RCIG sees on average a 108\% improvement over the previous state-of-the-art distillation algorithm. Similarly, we observed a 66\% gain over SOTA on Tiny-ImageNet and 37\% on CIFAR-100.

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Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus. 2023-02-13. Dataset Distillation with Convexified Implicit Gradients. https://arxiv.org/abs/2302.06755

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