42 citations · 59 across the 6 of their papers we have counts for
6 papers
HPN: Personalized Federated Hyperparameter Optimization
Anda Cheng, Zhen Wang, Yaliang Li +1
Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and cha…
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
Weihan Cao, Yifan Zhang, Jianfei Gao +3
Knowledge distillation(KD) is a widely-used technique to train compact models in object detection. However, there is still a lack of study on how to distill between heterogeneous d…
Differentially Private Federated Learning with Local Regularization and Sparsification
Anda Cheng, Peisong Wang, Xi Sheryl Zhang +1
User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning. Existing methods that ens…
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…
Location-aware Upsampling for Semantic Segmentation
Xiangyu He, Zitao Mo, Qiang Chen +3
Many successful learning targets such as minimizing dice loss and cross-entropy loss have enabled unprecedented breakthroughs in segmentation tasks. Beyond these semantic metrics,…
SpatialFlow: Bridging All Tasks for Panoptic Segmentation
Qiang Chen, Anda Cheng, Xiangyu He +2
Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene. Knowing the locations of objects in the image provides clues fo…