6 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 6 cited
Real-Fake: Effective Training Data Synthesis Through Distribution Matching
Jianhao Yuan, Jie Zhang, Shuyang Sun +2
Synthetic training data has gained prominence in numerous learning tasks and scenarios, offering advantages such as dataset augmentation, generalization evaluation, and privacy pre…
cs.LG2023★ 5 cited
Federated Generative Learning with Foundation Models
Jie Zhang, Xiaohua Qi, Bo Zhao
Existing approaches in Federated Learning (FL) mainly focus on sending model parameters or gradients from clients to a server. However, these methods are plagued by significant ine…
cs.LG2022★ 1 cited
Accelerating Dataset Distillation via Model Augmentation
Lei Zhang, Jie Zhang, Bowen Lei +6
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones. Existing DD methods based on gradient…