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Visualization of the Feature Space of Neural Networks

Author
Alaíz Gudin, Carlos Maríauntranslated; Fernández Pascual, Ángelauntranslated; Dorronsoro Ibero, José Ramónuntranslated
Entity
UAM. Departamento de Ingeniería Informática
Publisher
© ESANN 2020
Date
2020-10-04
Citation
ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Online event, 2-4 October 2020, i6doc.com publ., 169-174
 
 
 
ISBN
9782875870742
Funded by
With partial support from the European Regional Development Fund and from the Spanish Ministry of Economy, Industry, and Competitiveness, project TIN2016-76406-P (AEI/FEDER, UE). Work supported also by UAM–ADIC Chair for Data Science and Machine Learning. We also acknowledge the use of the facilities of Centro de Computacion Cientıfica (CCC) at UAM
Project
Gobierno de España. TIN2016-76406-P
Subjects
Informática
URI
http://hdl.handle.net/10486/703125
Rights
Copyright © ESANN, 2020

Abstract

Visualization of a learning machine can be crucial to understand its behaviour, specially in the case of (deep) neural networks, since they are quite difficult to interpret. An approach for visualizing the feature space of a neural network is presented, trying to answer to the question “what representation of the data is the network using to make its decision?” The proposed method gives a representation of the space where the network is tackling the problem, reducing it while respecting the linearity of the model. As shown experimentally, this technique allows to study the evolution of the model with respect to the training epochs, to have a representation of the data similar to the one used by the neural network, and even to detect groups of patterns that behave differently
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Google™ Scholar:Alaíz Gudin, Carlos María - Fernández Pascual, Ángela - Dorronsoro Ibero, José Ramón

This item appears in the following Collection(s)

  • Producción científica en acceso abierto de la UAM [17784]

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