Article

We introduce PRISM, a framework that disentangles architectural priors for dataset distillation, outperforming single-teacher setups.

We present HyperCore, a lightweight adaprive coreset selection framework designed for noisy environments. HyperCore utilizes per class hypersphere models and adaptively selects pruning thresholds.

We introduce SubZeroCore, a novel, training-free coreset selection method that integrates submodular coverage and density into a single, unified objective.

We improve the training of vision transformers by segmenting and recombining objects and backgrounds from datasets. This makes the transformers more accurate, as well as more robust.

We conduct the first systematic study of dataset distillation for Super-Resolution.

We improve dataset distillation by distilling only a representative coreset.

We speed up diffusion classifiers by utilizing a label hierarchy and pruning unrelated paths.

We utilize the TaylorShift attention mechanism for global pixel-wise-attention in image super-resolution.