Contemporary machine learning which involves training large neural networks on massive
datasets faces significant computational challenges. Dataset distillation as a recent …

Dataset pruning aims to construct a coreset capable of achieving performance comparable
to the original full dataset. Most existing dataset pruning methods rely on snapshot-based …

Recent works in dataset distillation seek to minimize training expenses by generating a
condensed synthetic dataset that encapsulates the information present in a larger real …

Dataset distillation (DD) entails creating a refined, compact distilled dataset from a large-
scale dataset to facilitate efficient training. A significant challenge in DD is the dependency …

In ImageNet-condensation, the storage for auxiliary soft labels exceeds that of the
condensed dataset by over 30 times. However, are large-scale soft labels necessary for …

Knowledge distillation (KD) is a key element in neural network compression that allows
knowledge transfer from a pre-trained teacher model to a more compact student model. KD …

This paper improves upon existing data pruning methods for image classification by
introducing a novel pruning metric and pruning procedure based on importance sampling …