Grounding and Enhancing Informativeness and Utility in Dataset Distillation
It addresses the challenge of creating compact datasets for machine learning, offering a novel approach that is incremental but provides specific gains in efficiency and quality.
This paper tackles the problem of dataset distillation by introducing a theoretical framework based on informativeness and utility, and presents InfoUtil, a method that achieves a 6.1% performance improvement over the previous state-of-the-art on ImageNet-1K with ResNet-18.
Dataset Distillation (DD) seeks to create a compact dataset from a large, real-world dataset. While recent methods often rely on heuristic approaches to balance efficiency and quality, the fundamental relationship between original and synthetic data remains underexplored. This paper revisits knowledge distillation-based dataset distillation within a solid theoretical framework. We introduce the concepts of Informativeness and Utility, capturing crucial information within a sample and essential samples in the training set, respectively. Building on these principles, we define optimal dataset distillation mathematically. We then present InfoUtil, a framework that balances informativeness and utility in synthesizing the distilled dataset. InfoUtil incorporates two key components: (1) game-theoretic informativeness maximization using Shapley Value attribution to extract key information from samples, and (2) principled utility maximization by selecting globally influential samples based on Gradient Norm. These components ensure that the distilled dataset is both informative and utility-optimized. Experiments demonstrate that our method achieves a 6.1\% performance improvement over the previous state-of-the-art approach on ImageNet-1K dataset using ResNet-18.