  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.
