Article

We show that newer text-to-image models are progressively worse as training data generators, despite better visual quality, because they collapse to a narrow aesthetic-centric distribution that diverges from real data.

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

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

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

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

We improve dataset distillation by distilling only a representative coreset.

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