4 papers
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…
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…