dataset distillation methods have demonstrated remarkable performance for
neural networks trained with very limited training data. However, a significant
challenge arises in the form of architecture overfitting:
Dataset Distillation technique using learned prior of deep generative models and a new optimization algorithm improves cross-architecture generalization by synthesizing few synthetic images from a large dataset.