GeoDM: Geometry-aware Distribution Matching for Dataset Distillation
Proposes GeoDM, a dataset distillation framework that performs distribution matching in a product space of Euclidean, hyperbolic, and spherical manifolds, with learnable curvature and weights. This geometry-aware approach yields lower generalization error bounds and consistently outperforms prior distillation methods by better aligning synthetic and real-data manifolds. ([arxiv.org](https://arxiv.org/abs/2512.08317?utm_source=openai))
Xuhui Li, Zhengquan Luo