26 citations · 41 across the 14 of their papers we have counts for
Showing 2023Show all
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cs.CV2023★ 1 cited
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Hansong Zhang, Shikun Li, Pengju Wang +2
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 h…
cs.CV2023★ 1 cited
Private Gradient Estimation is Useful for Generative Modeling
Bochao Liu, Pengju Wang, Weijia Guo +4
While generative models have proved successful in many domains, they may pose a privacy leakage risk in practical deployment. To address this issue, differentially private generati…
cs.CR2023
Model Conversion via Differentially Private Data-Free Distillation
Bochao Liu, Pengju Wang, Shikun Li +2
While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment whic…