Data pruning aims to obtain lossless performances with less overall cost. A common
approach is to filter out samples that make less contribution to the training. This could lead to …

The commercialization of text-to-image diffusion models (DMs) brings forth potential
copyright concerns. Despite numerous attempts to protect DMs from copyright issues, the …

Dataset distillation aims to compress information from a large-scale original dataset to a new
compact dataset while striving to preserve the utmost degree of the original data …

Dataset distillation is an advanced technique aimed at compressing datasets into
significantly smaller counterparts, while preserving formidable training performance …

Data heterogeneity presents significant challenges for federated learning (FL). Recently,
dataset distillation techniques have been introduced, and performed at the client level, to …

This paper presents our method for the generative track of The First Dataset Distillation
Challenge at ECCV 2024. Since the diffusion model has become the mainstay of generative …

Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic
dataset that captures the essential information of a training dataset, facilitating the training of …

Dataset Distillation aims to compress a large dataset into a significantly more compact,
synthetic one without compromising the performance of the trained models. To achieve this …

Dataset distillation, which condenses large-scale datasets into compact synthetic
representations, has emerged as a critical solution for training modern deep learning …

Dataset distillation has demonstrated strong performance on simple datasets like CIFAR,
MNIST, and TinyImageNet but struggles to achieve similar results in more complex …