1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation
Pengju Wang, Bochao Liu, Weijia Guo +2
Federated learning is a distributed machine learning paradigm designed to protect data privacy. However, data heterogeneity across various clients results in catastrophic forgettin…
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition
Junzheng Zhang, Weijia Guo, Bochao Liu +3
Very low-resolution face recognition is challenging due to the serious loss of informative facial details in resolution degradation. In this paper, we propose a generative-discrimi…
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation
Ruixin Shi, Weijia Guo, Shiming Ge
Low-resolution face recognition is a challenging task due to the missing of informative details. Recent approaches based on knowledge distillation have proven that high-resolution…
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
Bochao Liu, Pengju Wang, Shiming Ge
While the success of deep learning relies on large amounts of training datasets, data is often limited in privacy-sensitive domains. To address this challenge, generative model lea…