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 …

State-of-the-art deep neural networks are trained with large amounts (millions or even
billions) of data. The expensive computation and memory costs make it difficult to train them …

Continual learning (CL) can help pre-trained vision-language models efficiently adapt to
new or under-trained data distributions without re-training. Nevertheless, during the …

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 …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

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

The lightweight" local-match-global" matching introduced by SRe2L successfully creates a
distilled dataset with comprehensive information on the full 224x224 ImageNet-1k. However …

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 …

Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in
substantial training and storage costs. To address these challenges, dataset condensation …

Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing
a compact counterpart without sacrificing the performance of Graph Neural Networks …