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University of Applied Sciences of Western Switzerland (HES-SO) and SIB Swiss Institute of Bioinformatics

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1 Papers

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

Topics: Learning Paradigms and Algorithms; Self-Organization and Emergence; Deep Learning; 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

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

Digital Object Identifier: 10.5220/0006495102150222

Proceedings of IJCCI 2017/IJCCI, ISBN: 978-989-758-274-5

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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.

Related Ontology Subjects/Areas/Topics (18)

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