  As the state-of-the-art machine learning methods in many fields rely on
larger datasets, storing datasets and training models on them become
significantly more expensive. This paper proposes a training set synthesis
technique for data-efficient learning, called Dataset Condensation, that learns
to condense large dataset into a small set of informative synthetic samples for
training deep neural networks from scratch. We formulate this goal as a
gradient matching problem between the gradients of deep neural network weights
that are trained on the original and our synthetic data. We rigorously evaluate
its performance in several computer vision benchmarks and demonstrate that it
significantly outperforms the state-of-the-art methods. Finally we explore the
use of our method in continual learning and neural architecture search and
report promising gains when limited memory and computations are available.
