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Towards a Better Understanding of Deep Neural Networks Representations using Deep Generative Networks

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications; Deep Learning; Learning Paradigms and Algorithms; Self-Organization and Emergence

Authors: Jérémie Despraz 1 ; Stéphane Gomez 1 ; Héctor F. Satizábal 2 and Carlos Andrés Peña-Reyes 1

Affiliations: 1 University of Applied Sciences of Western Switzerland (HES-SO) and SIB Swiss Institute of Bioinformatics, Switzerland ; 2 University of Applied Sciences of Western Switzerland (HES-SO), Switzerland

Keyword(s): Deep-Learning, Convolutional Neural Networks, Generative Neural Networks, Activation Maximization, Interpretability.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Self-Organization and Emergence ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper presents a novel approach to deep-dream-like image generation for convolutional neural networks (CNNs). Images are produced by a deep generative network from a smaller dimensional feature vector. This method allows for the generation of more realistic looking images than traditional activation-maximization methods and gives insight into the CNN’s internal representations. Training is achieved by standard backpropagation algorithms.

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Paper citation in several formats:
Despraz, J.; Gomez, S.; Satizábal, H. and Peña-Reyes, C. (2017). Towards a Better Understanding of Deep Neural Networks Representations using Deep Generative Networks. In Proceedings of the 9th International Joint Conference on Computational Intelligence - IJCCI, ISBN 978-989-758-274-5; ISSN 2184-2825, pages 215-222. DOI: 10.5220/0006495102150222

@conference{ijcci17,
author={Jérémie Despraz. and Stéphane Gomez. and Héctor F. Satizábal. and Carlos Andrés Peña{-}Reyes.},
title={Towards a Better Understanding of Deep Neural Networks Representations using Deep Generative Networks},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - IJCCI,},
year={2017},
pages={215-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006495102150222},
isbn={978-989-758-274-5},
issn={2184-2825},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - IJCCI,
TI - Towards a Better Understanding of Deep Neural Networks Representations using Deep Generative Networks
SN - 978-989-758-274-5
IS - 2184-2825
AU - Despraz, J.
AU - Gomez, S.
AU - Satizábal, H.
AU - Peña-Reyes, C.
PY - 2017
SP - 215
EP - 222
DO - 10.5220/0006495102150222

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