Deep Generalized Max Pooling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43958239" target="_blank" >RIV/49777513:23520/19:43958239 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICDAR.2019.00177" target="_blank" >http://dx.doi.org/10.1109/ICDAR.2019.00177</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDAR.2019.00177" target="_blank" >10.1109/ICDAR.2019.00177</a>
Alternative languages
Result language
angličtina
Original language name
Deep Generalized Max Pooling
Original language description
Global pooling layers are an essential part of Convolutional Neural Networks (CNN). Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent. In contrast, we propose Deep Generalized Max Pooling that balances the contribution of all activations of a spatially coherent region by re-weighting all descriptors so that the impact of frequent and rare ones is equalized. We show that this layer is superior to both average and max pooling on the classification of Latin medieval manuscripts (CLAMM’16, CLAMM’17), as well as writer identification (Historical-WI’17).
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 15th IAPR International Conference on Document Analysis and Recognition
ISBN
978-1-72813-014-9
ISSN
1520-5363
e-ISSN
—
Number of pages
7
Pages from-to
1090-1096
Publisher name
IEEE
Place of publication
Piscataway
Event location
Austrálie, Sydney
Event date
Sep 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—