28 citations · 103 across the 19 of their papers we have counts for
7 papers · 1 filter
Personalized Federated Learning via Backbone Self-Distillation
Pengju Wang, Bochao Liu, Dan Zeng +2
In practical scenarios, federated learning frequently necessitates training personalized models for each client using heterogeneous data. This paper proposes a backbone self-distil…
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…
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
Bochao Liu, Jianghu Lu, Pengju Wang +4
Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical d…
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation
Kangkai Zhang, Shiming Ge, Ruixin Shi +1
Recognizing objects in low-resolution images is a challenging task due to the lack of informative details. Recent studies have shown that knowledge distillation approaches can effe…
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
Shiming Ge, Bochao Liu, Pengju Wang +2
While deep models have proved successful in learning rich knowledge from massive well-annotated data, they may pose a privacy leakage risk in practical deployment. It is necessary…
DANCE: Dual-View Distribution Alignment for Dataset Condensation
Hansong Zhang, Shikun Li, Fanzhao Lin +3
Dataset condensation addresses the problem of data burden by learning a small synthetic training set that preserves essential knowledge from the larger real training set. To date,…
Masked Face Recognition with Generative-to-Discriminative Representations
Shiming Ge, Weijia Guo, Chenyu Li +3
Masked face recognition is important for social good but challenged by diverse occlusions that cause insufficient or inaccurate representations. In this work, we propose a unified…