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 …

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 …

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

Dataset distillation (DD) is an increasingly important technique that focuses on constructing
a synthetic dataset capable of capturing the core information in training data to achieve …

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 …

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

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 offers an efficient way to reduce memory and computational costs by
optimizing a smaller dataset with performance comparable to the full-scale original …

Dataset distillation or condensation aims to generate a smaller but representative subset
from a large dataset, which allows a model to be trained more efficiently, meanwhile …