Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning

TL;DR

This paper formalizes dataset distillation (DD) as an optimization problem tied to specific inference tasks, revealing its broad applications and limitations, and demonstrating its potential in medical data merging and physics-informed neural networks.

Contribution

It introduces a formal, task-specific model of DD, enabling better understanding and development of DD methods across diverse applications.

Findings

Formal model of DD tied to inference tasks

Analysis of DD methods' strengths and limitations

Numerical case studies in medical data and physics-informed neural networks

Abstract

Dataset distillation (DD) is an increasingly important technique that focuses on constructing a synthetic dataset capable of capturing the core information in training data to achieve comparable performance in models trained on the latter. While DD has a wide range of applications, the theory supporting it is less well evolved. New methods of DD are compared on a common set of benchmarks, rather than oriented towards any particular learning task. In this work, we present a formal model of DD, arguing that a precise characterization of the underlying optimization problem must specify the inference task associated with the application of interest. Without this task-specific focus, the DD problem is under-specified, and the selection of a DD algorithm for a particular task is merely heuristic. Our formalization reveals novel applications of DD across different modeling environments. We analyze existing DD methods through this broader lens, highlighting their strengths and limitations in terms of accuracy and faithfulness to optimal DD operation. Finally, we present numerical results for two case studies important in contemporary settings. Firstly, we address a critical challenge in medical data analysis: merging the knowledge from different datasets composed of intersecting, but not identical, sets of features, in order to construct a larger dataset in what is usually a small sample setting. Secondly, we consider out-of-distribution error across boundary conditions for physics-informed neural networks (PINNs), showing the potential for DD to provide more physically faithful data. By establishing this general formulation of DD, we aim to establish a new research paradigm by which DD can be understood and from which new DD techniques can arise.