DAP formalizes a representativeness prior via Mercer kernel similarity in feature space and uses it to guide diffusion reverse process for higher-quality distilled datasets on ImageNet without retraining.
A.3.2 MODELS AND EVALUATION PROTOCOLS For each dataset, we distill subsets of 10, 50, and 100 images per class (IPC) and assess their utility on downstream classification tasks
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Diffusion Models as Dataset Distillation Priors
DAP formalizes a representativeness prior via Mercer kernel similarity in feature space and uses it to guide diffusion reverse process for higher-quality distilled datasets on ImageNet without retraining.