1 code implementation • 28 Sep 2022 • Haibo Yang, Shengjie Zhang, Xiaoyang Han, Botao Zhao, Yan Ren, Yaru Sheng, Xiao-Yong Zhang
We evaluate the proposed method on 720 T2-FLAIR brain images with small lesions at different noise levels.
1 code implementation • 19 Jul 2022 • Kailiang Zhong, Fengtong Xiao, Yan Ren, Yaorong Liang, Wenqing Yao, Xiaofeng Yang, Ling Cen
Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance.
no code implementations • 3 May 2022 • Yuxiang Feng, Weilin Xie, Yinxia Meng, Jiang Yang, Qiang Yang, Yan Ren, Tianwai Bo, Zhongwei Tan, Wei Wei, Yi Dong
Coherent fading has been regarded as a critical issue in phase-sensitive optical frequency domain reflectometry ({\phi}-OFDR) based distributed fiber-optic sensing.
1 code implementation • 5 Nov 2021 • Yuhang Li, Mingzhu Shen, Jian Ma, Yan Ren, Mingxin Zhao, Qi Zhang, Ruihao Gong, Fengwei Yu, Junjie Yan
Surprisingly, no existing algorithm wins every challenge in MQBench, and we hope this work could inspire future research directions.
no code implementations • 4 May 2021 • Qigong Sun, Xiufang Li, Yan Ren, Zhongjian Huang, Xu Liu, Licheng Jiao, Fang Liu
When the precision of quantization is adjusted, it is necessary to fine-tune the quantized model or minimize the quantization noise, which brings inconvenience in practical applications.
no code implementations • 9 Mar 2021 • Qigong Sun, Yan Ren, Licheng Jiao, Xiufang Li, Fanhua Shang, Fang Liu
Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method.
no code implementations • 4 Mar 2021 • Qigong Sun, Licheng Jiao, Yan Ren, Xiufang Li, Fanhua Shang, Fang Liu
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart chips.
no code implementations • 31 May 2019 • Qigong Sun, Fanhua Shang, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance.
no code implementations • 7 Oct 2018 • Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin
This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick.