DRUPI: Dataset Reduction Using Privileged Information

ArXi:2410.01611v3 Announce Type: replace-cross Dataset Condensation (DC) seeks to select or distill samples from large datasets into smaller subsets while preserving performance on target tasks. Existing methods primarily focus on pruning or synthesizing data in the same format as the original dataset, typically being the input data and corresponding labels. However, in DC settings, we find it is possible to synthesize information beyond the data-label pair as an additional learning target to facilitate model