Dataset Distillation by Matching Training Trajectories

By George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu. 2022

606 citations recorded by the source corpus.

Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Efficiency & Compression, Optimization & Training

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Citations in the corpus

Learning to Learn with Generative Models of Neural Network Checkpoints (2022, background): “…population-level trends (Radosavovic et al., 2019; 2020), benchmarking neural architecture search (Ying et al., 2019), training hypernetworks (Knyazev et al., 2021), predicting model properties (Sch¨urholt et al., 2021) and dataset distillation (Wang et al., 2018; Cazenavette et al., 2022).”