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Authors: Jyoti Nigam ; Srishti Barahpuriya and Renu M. Rameshan

Affiliation: Indian Institute of Technology, Mandi, Himachal Pradesh and India

Keyword(s): Convolution, Correlation, Linear Transformation, Nonlinear Transformation.

Abstract: AlexNet, one of the earliest and successful deep learning networks, has given great performance in image classification task. There are some fundamental properties for good classification such as: the network preserves the important information of the input data; the network is able to see differently, points from different classes. In this work we experimentally verify that these core properties are followed by the AlexNet architecture. We analyze the effect of linear and nonlinear transformations on input data across the layers. The convolution filters are modeled as linear transformations. The verified results motivate to draw conclusions on the desirable properties of transformation matrix that aid in better classification.

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Paper citation in several formats:
Nigam, J.; Barahpuriya, S. and Rameshan, R. (2019). Analyzing the Linear and Nonlinear Transformations of AlexNet to Gain Insight into Its Performance. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 860-865. DOI: 10.5220/0007582408600865

@conference{icpram19,
author={Jyoti Nigam. and Srishti Barahpuriya. and Renu M. Rameshan.},
title={Analyzing the Linear and Nonlinear Transformations of AlexNet to Gain Insight into Its Performance},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={860-865},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007582408600865},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Analyzing the Linear and Nonlinear Transformations of AlexNet to Gain Insight into Its Performance
SN - 978-989-758-351-3
IS - 2184-4313
AU - Nigam, J.
AU - Barahpuriya, S.
AU - Rameshan, R.
PY - 2019
SP - 860
EP - 865
DO - 10.5220/0007582408600865
PB - SciTePress