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Detail publikačního výsledku
KOLÁŘ, M.; HRADIŠ, M.; ZEMČÍK, P.
Originální název
Deep Learning on Small Datasets using Online Image Search
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
Originální abstrakt
Our contribution has the ability to learn visual categories from fewer images than previous approaches. We do this by modifying the pseudolabel method which augments labelled training images with unlabelled images, to create a method capable of handling labelled training images as well as queried images, which are likely to belong to the desired class. This is achieved by modifying the weighting and selection processes.The presented method adapts the pseudolabel approach to allow the use of web-scale datasets of millions of images. The results are demonstrated on a toy problem&start=0&order=1 devised from the SUN 397 dataset, and on the full SUN 397 dataset expanded with images gathered from Google’s image search without human intervention.
Anglický abstrakt
Klíčová slova
convolutional neural network, deep learning, image classification, reinforcement learning
Klíčová slova v angličtině
Autoři
Rok RIV
2017
Vydáno
04.04.2016
Nakladatel
Comenius University in Bratislava
Místo
Bratislava
ISBN
978-1-4503-3693-2
Kniha
Proceedings of 32nd Spring Conference on Computer Graphics
ISSN
1335-5694
Periodikum
Proceeding of Spring Conference on Computer Graphics
Svazek
2016
Číslo
32
Stát
Slovenská republika
Strany od
87
Strany do
93
Strany počet
7
URL
http://dl.acm.org/citation.cfm?id=2948633
BibTex
@inproceedings{BUT130963, author="Martin {Kolář} and Michal {Hradiš} and Pavel {Zemčík}", title="Deep Learning on Small Datasets using Online Image Search", booktitle="Proceedings of 32nd Spring Conference on Computer Graphics", year="2016", journal="Proceeding of Spring Conference on Computer Graphics", volume="2016", number="32", pages="87--93", publisher="Comenius University in Bratislava", address="Bratislava", doi="10.1145/2948628.2948633", isbn="978-1-4503-3693-2", issn="1335-5694", url="http://dl.acm.org/citation.cfm?id=2948633" }
Dokumenty
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