The author introduces an algorithm inspired by distributionally robust optimization to enhance dataset distillation, focusing on generalization and robustness across subgroups. The approach combines clustering with risk minimization for effective performance.
Aligning the difficulty of generated patterns with the size of the synthetic dataset enables lossless dataset distillation.
Incorporating generative diffusion techniques enhances representativeness and diversity in dataset distillation.