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HORÁK, K.
Originální název
Deep Learning Concepts and Datasets for Image Recognition: Overview 2019
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
Originální abstrakt
We present basics of a deep learning concept and an overview of well-known deep learning concepts as general Convolutional Neural Networks, R-CNN family, Single Shot Multibox Detector, You Only Look Once architecture and the RetinaNet in the first part of this paper. The all mentioned architectures are described to quickly compare to each other regarding their suitability for given general task. Several selected datasets often used in deep learning competitions are listed in the subsequent chapters in more details. The most known of practically used and listed datasets are COCO, KITTI, PascalVOC and CityShapes. The overview serves as a comparison of the state-of-the-art deep learning methods.
Anglický abstrakt
Klíčová slova
Deep learning, dataset, image recognition, convolutional neural network, R-CNN, RetinaNet.
Klíčová slova v angličtině
Autoři
Rok RIV
2020
Vydáno
14.08.2019
Nakladatel
SPIE
Místo
Guangzhou, China
ISBN
9781510630758
Kniha
Proceedings of SPIE - The International Society for Optical Engineering
Edice
Volume 11179
ISSN
0277-786X
Periodikum
Proceedings of SPIE
Svazek
11179
Číslo
111791S
Stát
Spojené státy americké
Strany od
484
Strany do
491
Strany počet
8
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072601173&doi=10.1117%2f12.2539806&partnerID=40&md5=250c8c0254e4037ba7340dc71d2c6f09
BibTex
@inproceedings{BUT159588, author="Karel {Horák}", title="Deep Learning Concepts and Datasets for Image Recognition: Overview 2019", booktitle="Proceedings of SPIE - The International Society for Optical Engineering", year="2019", series="Volume 11179", journal="Proceedings of SPIE", volume="11179", number="111791S", pages="484--491", publisher="SPIE", address="Guangzhou, China", doi="10.1117/12.2539806", isbn="9781510630758", issn="0277-786X", url="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072601173&doi=10.1117%2f12.2539806&partnerID=40&md5=250c8c0254e4037ba7340dc71d2c6f09" }
Dokumenty
ICDIP_2019_ID116_Horak