The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a
model trained on this synthetic set will perform equally well as a model trained on the full …

Data-efficient learning has garnered significant attention, especially given the current trend
of large multi-modal models. Recently, dataset distillation has become an effective approach …

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 …

Recent works in dataset distillation seek to minimize training expenses by generating a
condensed synthetic dataset that encapsulates the information present in a larger real …

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) aims to minimize the time and memory consumption needed for
training deep neural networks on large datasets, by creating a smaller synthetic dataset that …

With the rapid scaling of neural networks, data storage and communication demands have
intensified. Dataset distillation has emerged as a promising solution, condensing information …

Recent advances in dataset distillation have led to solutions in two main directions. The
conventional batch-to-batch matching mechanism is ideal for small-scale datasets and …

Condensing large datasets into smaller synthetic counterparts has demonstrated its promise
for image classification. However, previous research has overlooked a crucial concern in …

Learning from noisy data has become essential for adapting deep learning models to real-
world applications. Traditional methods often involve first evaluating the noise and then …