no code implementations • 14 May 2024 • Jie Fu, Yuan Hong, XinPeng Ling, Leixia Wang, Xun Ran, Zhiyu Sun, Wendy Hui Wang, Zhili Chen, Yang Cao
Our work presents a systematic overview of the differentially private federated learning.
no code implementations • 10 Mar 2024 • Zhili Chen, Kien T. Pham, Maosheng Ye, Zhiqiang Shen, Qifeng Chen
We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving.
2 code implementations • 23 Nov 2023 • Jie Fu, Qingqing Ye, Haibo Hu, Zhili Chen, Lulu Wang, Kuncan Wang, Xun Ran
Motivated by this, this paper proposes DPSUR, a Differentially Private training framework based on Selective Updates and Release, where the gradient from each iteration is evaluated based on a validation test, and only those updates leading to convergence are applied to the model.
no code implementations • 14 Nov 2023 • Zhili Chen, Maosheng Ye, Shuangjie Xu, Tongyi Cao, Qifeng Chen
Unlike existing end-to-end autonomous driving frameworks, PPAD models the interactions among ego, agents, and the dynamic environment in an autoregressive manner by interleaving the Prediction and Planning processes at every timestep, instead of a single sequential process of prediction followed by planning.
no code implementations • 6 Nov 2023 • Huifa Li, Jie Fu, Zhili Chen, Xiaomin Yang, Haitao Liu, XinPeng Ling
Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression.
1 code implementation • 21 Aug 2023 • XinPeng Ling, Jie Fu, Kuncan Wang, Haitao Liu, Zhili Chen
Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data.
no code implementations • 29 Nov 2022 • Jie Fu, Zhili Chen, Xiao Han
The heterogeneity and convergence of training parameters were simply not considered.
no code implementations • 14 Nov 2022 • Jie Fu, Zhili Chen, XinPeng Ling
Differentially private stochastic gradient descent (DPSGD) is the most popular training method with differential privacy in image recognition.
1 code implementation • 2 May 2022 • Zhili Chen, Zian Qian, Sukai Wang, Qifeng Chen
We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way.
1 code implementation • 7 Aug 2020 • Yichao Zhou, Jingwei Huang, Xili Dai, Shichen Liu, Linjie Luo, Zhili Chen, Yi Ma
We present HoliCity, a city-scale 3D dataset with rich structural information.
1 code implementation • CVPR 2020 • Kyle Olszewski, Duygu Ceylan, Jun Xing, Jose Echevarria, Zhili Chen, Weikai Chen, Hao Li
We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects.
1 code implementation • 24 Feb 2020 • Tianlang Chen, Chen Fang, Xiaohui Shen, Yiheng Zhu, Zhili Chen, Jiebo Luo
In this work, we propose a new solution to 3D human pose estimation in videos.
Ranked #12 on Monocular 3D Human Pose Estimation on Human3.6M
2 code implementations • ICCV 2019 • Yichao Zhou, Haozhi Qi, Yuexiang Zhai, Qi Sun, Zhili Chen, Li-Yi Wei, Yi Ma
In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities.
no code implementations • 19 Oct 2018 • Shun Zhang, Laixiang Liu, Zhili Chen, Hong Zhong
The results show that the PDP-PMF scheme performs well on protecting the privacy of each user and its recommendation quality is much better than the DP-PMF scheme.
no code implementations • 14 Oct 2018 • Tao Zhou, Chen Fang, Zhaowen Wang, Jimei Yang, Byungmoon Kim, Zhili Chen, Jonathan Brandt, Demetri Terzopoulos
Doodling is a useful and common intelligent skill that people can learn and master.