Deep learning technology has developed unprecedentedly in the last decade and has
become the primary choice in many application domains. This progress is mainly attributed …

Dataset condensation is a crucial tool for enhancing training efficiency by reducing the size
of the training dataset, particularly in on-device scenarios. However, these scenarios have …

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 …

Dataset distillation is a newly emerging task that synthesizes a small-size dataset used in
training deep neural networks (DNNs) for reducing data storage and model training costs …

In-context learning is a promising paradigm that utilizes in-context examples as prompts for
the predictions of large language models. These prompts are crucial for achieving strong …

Existing dataset distillation (DD) techniques typically rely on iterative strategies to synthesize
condensed datasets, where datasets before and after distillation are forward and backward …

Dataset distillation reduces the storage and computational consumption of training a
network by generating a small surrogate dataset that encapsulates rich information of the …

Dataset distillation methods offer the promise of reducing a large-scale dataset down to a
significantly smaller set of (potentially synthetic) training examples, which preserve sufficient …

With the increasing demands of training graph neural networks (GNNs) on large-scale
graphs, graph data condensation has emerged as a critical technique to relieve the storage …

Dataset distillation aims at a small synthetic dataset to mimic the training performance on
neural networks of a given large dataset. Existing approaches heavily rely on an iterative …