28 citations · 103 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
Coupled Confusion Correction: Learning from Crowds with Sparse Annotations
Hansong Zhang, Shikun Li, Dan Zeng +2
As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sou…
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm
Shikun Li, Xiaobo Xia, Hansong Zhang +2
Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative…
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
Shikun Li, Xiaobo Xia, Jiankang Deng +2
Learning from crowds describes that the annotations of training data are obtained with crowd-sourcing services. Multiple annotators each complete their own small part of the annota…
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…
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…