GeoDM: Geometry-aware Distribution Matching for Dataset Distillation

TL;DR

GeoDM introduces a geometry-aware distribution matching framework for dataset distillation that operates in mixed geometric spaces, capturing intrinsic data structures to improve fidelity and performance.

Contribution

It proposes a novel unified framework in product manifolds with learnable curvature, enhancing distribution matching for dataset distillation.

Findings

Outperforms state-of-the-art methods on benchmarks

Effective across various geometric distribution-matching strategies

Theoretically achieves smaller generalization error bounds

Abstract

Dataset distillation aims to synthesize a compact subset of the original data, enabling models trained on it to achieve performance comparable to those trained on the original large dataset. Existing distribution-matching methods are confined to Euclidean spaces, making them only capture linear structures and overlook the intrinsic geometry of real data, e.g., curvature. However, high-dimensional data often lie on low-dimensional manifolds, suggesting that dataset distillation should have the distilled data manifold aligned with the original data manifold. In this work, we propose a geometry-aware distribution-matching framework, called \textbf{GeoDM}, which operates in the Cartesian product of Euclidean, hyperbolic, and spherical manifolds, with flat, hierarchical, and cyclical structures all captured by a unified representation. To adapt to the underlying data geometry, we introduce learnable curvature and weight parameters for three kinds of geometries. At the same time, we design an optimal transport loss to enhance the distribution fidelity. Our theoretical analysis shows that the geometry-aware distribution matching in a product space yields a smaller generalization error bound than the Euclidean counterparts. Extensive experiments conducted on standard benchmarks demonstrate that our algorithm outperforms state-of-the-art data distillation methods and remains effective across various distribution-matching strategies for the single geometries.