Semi-supervised Training of Deep Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F13%3APU108096" target="_blank" >RIV/00216305:26230/13:PU108096 - isvavai.cz</a>
Result on the web
<a href="http://www.fit.vutbr.cz/research/groups/speech/publi/2013/vesely_asru2013_0000267.pdf" target="_blank" >http://www.fit.vutbr.cz/research/groups/speech/publi/2013/vesely_asru2013_0000267.pdf</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Semi-supervised Training of Deep Neural Networks
Original language description
Our quest in this paper is to search for an optimal dataselection strategy for the semi-supervised DNN training. We performed an analysis at all the three stages of DNN training.
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/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of ASRU 2013
ISBN
978-1-4799-2755-5
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
267-272
Publisher name
IEEE Signal Processing Society
Place of publication
Olomouc
Event location
Olomouc
Event date
Dec 8, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—