  Using huge training datasets can be costly and inconvenient. This article
explores various data distillation techniques that can reduce the amount of
data required to successfully train deep networks. Inspired by recent ideas, we
suggest new data distillation techniques based on generative teaching networks,
gradient matching, and the Implicit Function Theorem. Experiments with the
MNIST image classification problem show that the new methods are
computationally more efficient than previous ones and allow to increase the
performance of models trained on distilled data.
