Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137022" target="_blank" >RIV/00216305:26220/20:PU137022 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9163397" target="_blank" >https://ieeexplore.ieee.org/document/9163397</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP49548.2020.9163397" target="_blank" >10.1109/TSP49548.2020.9163397</a>
Alternative languages
Result language
angličtina
Original language name
Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks
Original language description
The widespread use of Batch Normalization has enabled training deeper neural networks with more stable and faster results. However, the Batch Normalization works best using large batch size during training and as the state-of-theart segmentation convolutional neural network architectures are very memory demanding, large batch size is often impossible to achieve on current hardware. We evaluate the alternative normalization methods proposed to solve this issue on a problem of binary spine segmentation from 3D CT scan. Our results show the effectiveness of Instance Normalization in the limited batch size neural network training environment. Out of all the compared methods the Instance Normalization achieved the highest result with Dice coefficient = 0.96 which is comparable to our previous results achieved by deeper network with longer training time. We also show that the Instance Normalization implementation used in this experiment is computational timeefficient when compared to the network without any normalization method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 43rd International Conference on Telecommunications and Signal Processing (TSP)
ISBN
978-1-7281-6376-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
677-680
Publisher name
Neuveden
Place of publication
Neuveden
Event location
Milan, Italy
Event date
Jul 7, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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