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Authors: Gabriel D. Cantareira 1 ; Fernando V. Paulovich 2 and Elham Etemad 2

Affiliations: 1 Universidade de São Paulo, Brazil ; 2 Dalhousie University, Canada

Keyword(s): Machine Learning, Neural Network Visualization, Deep Neural Networks.

Abstract: Analyzing and understanding how abstract representations of data are formed inside deep neural networks is a complex task. Among the different methods that have been developed to tackle this problem, multidimensional projection techniques have attained positive results in displaying the relationships between data instances, network layers or class features. However, these techniques are often static and lack a way to properly keep a stable space between observations and properly convey flow in such space. In this paper, we employ different dimensionality reduction techniques to create a visual space where the flow of information inside hidden layers can come to light. We discuss the application of each used tool and provide experiments that show how they can be combined to highlight new information about neural network optimization processes.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Cantareira, G.; Paulovich, F. and Etemad, E. (2020). Visualizing Learning Space in Neural Network Hidden Layers. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 110-121. DOI: 10.5220/0009168901100121

@conference{ivapp20,
author={Gabriel D. Cantareira. and Fernando V. Paulovich. and Elham Etemad.},
title={Visualizing Learning Space in Neural Network Hidden Layers},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP},
year={2020},
pages={110-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009168901100121},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP
TI - Visualizing Learning Space in Neural Network Hidden Layers
SN - 978-989-758-402-2
IS - 2184-4321
AU - Cantareira, G.
AU - Paulovich, F.
AU - Etemad, E.
PY - 2020
SP - 110
EP - 121
DO - 10.5220/0009168901100121
PB - SciTePress

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