Distributional Dataset Distillation with Subtask Decomposition

arXiv:2403.00999v1 Announce Type: new
Abstract: What does a neural network learn when training from a task-specific dataset? Synthesizing this knowledge is the central idea behind Dataset Distillation, which recent work has shown can be used to compress large datasets into a small set of input-label pairs ($\textit{prototypes}$) that capture essential aspects of the original dataset. In this paper, we make the key observation that existing methods distilling into explicit prototypes are very often suboptimal, incurring in unexpected storage cost from …

abstract arxiv cs.lg dataset datasets distillation knowledge large datasets learn network neural network set small training type work