3 code implementations • 17 Aug 2022 • Zhijun Tu, Xinghao Chen, Pengju Ren, Yunhe Wang
Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high.
no code implementations • 7 Jun 2021 • Badong Chen, Yunfei Zheng, Pengju Ren
A novel model called error loss network (ELN) is proposed to build an error loss function for supervised learning.
2 code implementations • CVPR 2020 • Zhiwei Dong, Guoxuan Li, Yue Liao, Fei Wang, Pengju Ren, Chen Qian
CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned.
1 code implementation • 4 Jan 2020 • Zirui Zhao, Yijun Mao, Yan Ding, Pengju Ren, Nanning Zheng
Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction.
no code implementations • 21 Nov 2019 • Badong Chen, Yuqing Xie, Zhuang Li, Yingsong Li, Pengju Ren
Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables.
no code implementations • 24 May 2019 • Badong Chen, Yuqing Xie, Xin Wang, Zejian yuan, Pengju Ren, Jing Qin
In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths.