40 citations · 132 across the 19 of their papers we have counts for
6 papers · 1 filter
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Jiahao Lu, Xi Sheryl Zhang, Tianli Zhao +2
Federated learning frameworks typically require collaborators to share their local gradient updates of a common model instead of sharing training data to preserve privacy. However,…
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy
Anda Cheng, Jiaxing Wang, Xi Sheryl Zhang +3
Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNN…
Improving Binary Neural Networks through Fully Utilizing Latent Weights
Weixiang Xu, Qiang Chen, Xiangyu He +2
Binary Neural Networks (BNNs) rely on a real-valued auxiliary variable W to help binary training. However, pioneering binary works only use W to accumulate gradient updates during…
IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition
Chengzhi Jiang, Yanzhou Su, Wen Wang +3
Existing classification-based face recognition methods have achieved remarkable progress, introducing large margin into hypersphere manifold to learn discriminative facial represen…
HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap
Xing Lan, Qinghao Hu, Qiang Chen +2
Heatmap-based regression overcomes the lack of spatial and contextual information of direct coordinate regression, and has revolutionized the task of face alignment. Yet it suffers…
You Only Look One-level Feature
Qiang Chen, Yingming Wang, Tong Yang +3
This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization pro…