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Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks. / Smirnov, Evgeny A.; Timoshenko, Denis M.; Andrianov, Serge N.

In: AASRI Procedia, Vol. 6, 2014, p. 89-94.

Research output: Contribution to journal › Article

Harvard

Smirnov, EA, Timoshenko, DM & Andrianov, SN 2014, 'Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks', AASRI Procedia, vol. 6, pp. 89-94. <http://www.sciencedirect.com/science/article/pii/S2212671614000146>

APA

Smirnov, E. A., Timoshenko, D. M., & Andrianov, S. N. (2014). Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks. AASRI Procedia, 6, 89-94. http://www.sciencedirect.com/science/article/pii/S2212671614000146

Vancouver

Smirnov EA, Timoshenko DM, Andrianov SN. Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks. AASRI Procedia. 2014;6:89-94.

Author

Smirnov, Evgeny A. ; Timoshenko, Denis M. ; Andrianov, Serge N. / Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks. In: AASRI Procedia. 2014 ; Vol. 6. pp. 89-94.

BibTeX

@article{b4de9e39eb7543a498c28fa902872a50,
title = "Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks",
abstract = "Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.",
keywords = "Deep Neural Networks, Convolutional Neural Networks, Dropout, DropConnect, ImageNet",
author = "Smirnov, {Evgeny A.} and Timoshenko, {Denis M.} and Andrianov, {Serge N.}",
year = "2014",
language = "English",
volume = "6",
pages = "89--94",
journal = "AASRI Procedia",
issn = "2212-6716",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks

AU - Smirnov, Evgeny A.

AU - Timoshenko, Denis M.

AU - Andrianov, Serge N.

PY - 2014

Y1 - 2014

N2 - Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.

AB - Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.

KW - Deep Neural Networks

KW - Convolutional Neural Networks

KW - Dropout

KW - DropConnect

KW - ImageNet

M3 - Article

VL - 6

SP - 89

EP - 94

JO - AASRI Procedia

JF - AASRI Procedia

SN - 2212-6716

ER -

ID: 5699727