activity
20192022
most citedPoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

23 citations · 46 across the 11 of their papers we have counts for

collaborators

15 papers

cs.CV2022

Improving Crowded Object Detection via Copy-Paste

Jiangfan Deng, Dewen Fan, Xiaosong Qiu +1

Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of…

cs.LG20225 cited

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

Zhijie Deng, Feng Zhou, Jun Zhu

Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks. The generalized Gauss-Newto…

cs.LG20223 cited

Deep Ensemble as a Gaussian Process Approximate Posterior

Zhijie Deng, Feng Zhou, Jianfei Chen +2

Deep Ensemble (DE) is an effective alternative to Bayesian neural networks for uncertainty quantification in deep learning. The uncertainty of DE is usually conveyed by the functio…

cs.CV202123 cited

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

Qiang Meng, Xiaqing Xu, Xiaobo Wang +6

Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g.…

cs.LG2021

Bias-Tolerant Fair Classification

Yixuan Zhang, Feng Zhou, Zhidong Li +2

The label bias and selection bias are acknowledged as two reasons in data that will hinder the fairness of machine-learning outcomes. The label bias occurs when the labeling decisi…

cs.LG2021

Nonlinear Hawkes Processes in Time-Varying System

Feng Zhou, Quyu Kong, Yixuan Zhang +2

Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of…