28 citations · 59 across the 13 of their papers we have counts for
6 papers · 1 filter
Privacy-Preserving Model Transcription with Differentially Private Synthetic Distillation
Bochao Liu, Shiming Ge, Pengju Wang +2
While many deep learning models trained on private datasets have been deployed in various practical tasks, they may pose a privacy leakage risk as attackers could recover informati…
Federated Learning with Label-Masking Distillation
Jianghu Lu, Shikun Li, Kexin Bao +3
Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In…
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…
Trustable Co-label Learning from Multiple Noisy Annotators
Shikun Li, Tongliang Liu, Jiyong Tan +2
Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multipl…
Student Network Learning via Evolutionary Knowledge Distillation
Kangkai Zhang, Chunhui Zhang, Shikun Li +2
Knowledge distillation provides an effective way to transfer knowledge via teacher-student learning, where most existing distillation approaches apply a fixed pre-trained model as…