OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation

In one paragraph

Argues that sample-generation-based distillation methods give every real instance equal, uniform contribution when shaping each synthetic sample and so ignore instance-level real-synthetic relationships, and reformulates the matching objective as a bi-level matching-and-approximating problem where an optimal-transport matrix allocates contributions from real instances before the synthetic samples are refined against that allocation; reports gains across seven datasets and three architectures as a plug-in compatible with multiple distillation frameworks.