Contemporary machine learning which involves training large neural networks on massive
datasets faces significant computational challenges. Dataset distillation as a recent …

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 …

Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that
preserves essential information from the original dataset. This synthetic dataset can serve as …

Training high-quality deep models necessitates vast amounts of data, resulting in
overwhelming computational and memory demands. Recently, data pruning, distillation, and …

Dataset distillation refers to the process of constructing a smaller dataset based on a larger
dataset, so that the training model with the smaller dataset can obtain similar results to the …

Replay-based methods have proved their effectiveness on online continual learning by
rehearsing past samples from an auxiliary memory. With many efforts made on improving …

Recently, deep learning technology has been successfully introduced into Automatic
Modulation Recognition (AMR) tasks. However, the success of deep learning is all attributed …