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1906.03548
Cited By
Four Things Everyone Should Know to Improve Batch Normalization
9 June 2019
Cecilia Summers
M. Dinneen
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Papers citing
"Four Things Everyone Should Know to Improve Batch Normalization"
14 / 14 papers shown
Title
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
Divyam Madaan
D. Sodickson
K. Cho
S. Chopra
OOD
MedIm
35
1
0
23 Jun 2023
Ghost Noise for Regularizing Deep Neural Networks
Atli Kosson
Dongyang Fan
Martin Jaggi
25
1
0
26 May 2023
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
Hyesu Lim
Byeonggeun Kim
Jaegul Choo
Sungha Choi
TTA
29
85
0
10 Feb 2023
A New Perspective for Understanding Generalization Gap of Deep Neural Networks Trained with Large Batch Sizes
O. Oyedotun
Konstantinos Papadopoulos
Djamila Aouada
AI4CE
32
11
0
21 Oct 2022
NoMorelization: Building Normalizer-Free Models from a Sample's Perspective
Chang-Shu Liu
Yuwen Yang
Yue Ding
Hongtao Lu
34
2
0
13 Oct 2022
Understanding and Improving Group Normalization
Agus Gunawan
Xu Yin
Kang Zhang
15
3
0
05 Jul 2022
Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks
Bum Jun Kim
Hyeyeon Choi
Hyeonah Jang
Dong Gu Lee
Wonseok Jeong
Sang Woo Kim
19
4
0
15 May 2022
LTD: Low Temperature Distillation for Robust Adversarial Training
Erh-Chung Chen
Che-Rung Lee
AAML
27
26
0
03 Nov 2021
Batch Normalization Preconditioning for Neural Network Training
Susanna Lange
Kyle E. Helfrich
Qiang Ye
27
9
0
02 Aug 2021
"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization
Tianlong Chen
Zhenyu Zhang
Xu Ouyang
Zechun Liu
Zhiqiang Shen
Zhangyang Wang
MQ
43
36
0
16 Apr 2021
High-Performance Large-Scale Image Recognition Without Normalization
Andrew Brock
Soham De
Samuel L. Smith
Karen Simonyan
VLM
223
512
0
11 Feb 2021
Bag of Tricks for Image Classification with Convolutional Neural Networks
Tong He
Zhi-Li Zhang
Hang Zhang
Zhongyue Zhang
Junyuan Xie
Mu Li
221
1,400
0
04 Dec 2018
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao
Yasaman Bahri
Jascha Narain Sohl-Dickstein
S. Schoenholz
Jeffrey Pennington
244
348
0
14 Jun 2018
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
271
5,327
0
05 Nov 2016
1