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Zhanglin Peng
Researcher at SenseTime
Publications - 30
Citations - 647
Zhanglin Peng is an academic researcher from SenseTime. The author has contributed to research in topics: Normalization (statistics) & Artificial neural network. The author has an hindex of 12, co-authored 27 publications receiving 485 citations. Previous affiliations of Zhanglin Peng include Sun Yat-sen University.
Papers
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Proceedings Article
Differentiable Learning-to-Normalize via Switchable Normalization
TL;DR: Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network, is proposed, which will help ease the usage and understand the normalization techniques in deep learning.
Posted Content
Towards Understanding Regularization in Batch Normalization
TL;DR: Batch Normalization improves both convergence and generalization in training neural networks and is analyzed by using a basic block of neural networks, consisting of a kernel layer, a BN layer, and a nonlinear activation function.
Journal ArticleDOI
Switchable Normalization for Learning-to-Normalize Deep Representation
TL;DR: Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network, is proposed and will help ease the usage and understand the normalization techniques in deep learning.
Posted Content
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov,Radu Timofte,Zhilu Zhang,Ming Liu,Haolin Wang,Wangmeng Zuo,Jiawei Zhang,Ruimao Zhang,Zhanglin Peng,Sijie Ren,Linhui Dai,Xiaohong Liu,Chengqi Li,Jun Chen,Yuichi Ito,Bhavya Vasudeva,Puneesh Deora,Umapada Pal,Zhenyu Guo,Yu Zhu,Tian Liang,Chenghua Li,Cong Leng,Zhihong Pan,Baopu Li,Byung-Hoon Kim,Joonyoung Song,Jong Chul Ye,JaeHyun Baek,Magauiya Zhussip,Yeskendir Koishekenov,Hwechul Cho Ye,Xin Liu,Xueying Hu,Jun Jiang,Jinwei Gu,Kai Li,Pengliang Tan,Bingxin Hou +38 more
TL;DR: This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.
Proceedings Article
Towards Understanding Regularization in Batch Normalization
TL;DR: In this paper, a basic block of neural networks, consisting of a kernel layer, a BN layer, and a nonlinear activation function, is used to understand the impacts of batch normalization in training neural networks.