2018 • In Meneses, Esteban; Castro, Harold; Barrios Hernández, Carlos Jaimeet al. (Eds.) High Performance Computing -- 5th Latin American Conference, CARLA 2018, Piedecuesta, Colombia
[en] Deep Learning is based on deep neural networks trained over huge sets of examples.
It enabled computers to compete with ---~or even outperform~--- humans at many tasks, from playing Go to driving vehicules.
Still, it remains hard to understand how these networks actually operate.
While an observer sees any individual local behaviour, he gets little insight about their global decision-making process.
However, there is a class of neural networks widely used for image processing, convolutional networks, where each layer contains features working in parallel.
By their structure, these features keep some spatial information across a network's layers.
Visualisation of this spatial information at different locations in a network, notably on input data that maximise the activation of a given feature, can give insights on the way the model works.
This paper investigates the use of Evolutionary Algorithms to evolve such input images that maximise feature activation.
Compared with some pre-existing approaches, ours seems currently computationally heavier but with a wider applicability.
Centre de recherche :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Sciences informatiques
Auteur, co-auteur :
BERNARD, Nicolas ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
LEPRÉVOST, Franck ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Evolutionary Algorithms for Convolutional Neural Network Visualisation
Date de publication/diffusion :
2018
Nom de la manifestation :
CARLA 2018 - 5th Latin America High Performance Computing Conference
Lieu de la manifestation :
Piedecuesta, Colombie
Date de la manifestation :
from 23-09-2018 to 28-09-2018
Titre de l'ouvrage principal :
High Performance Computing -- 5th Latin American Conference, CARLA 2018, Piedecuesta, Colombia
Editeur scientifique :
Meneses, Esteban
Castro, Harold
Barrios Hernández, Carlos Jaime
Ramos-Pollan, Raúl
Maison d'édition :
Springer
ISBN/EAN :
978-3-030-16204-7
Collection et n° de collection :
Communications in Computer and Information Science
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. CoRR abs/1311.2901 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Dyson, F.: A meeting with Enrico Fermi. Nature 427, 297 (2004). https://doi.org/10.1038/427297a
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR abs/1312.6034 (2013)
Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015. https://www.cv-foundation.org/openaccess/content cvpr 2015/html/Mahendran Understanding Deep Image 2015 CVPR paper.html
Mordvintsev, A., Olah, C., Tyka, M.: Inceptionism: going deeper into neural networks. Google AI Blog, June 2015. https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html
Mordvintsev, A., Tyka, M., Olah, C.: DeepDream. GitHub code repository. https://github.com/google/deepdream
Yosinski, J., Clune, J., Nguyen, A.M., Fuchs, T.J., Lipson, H.: Understanding neural networks through deep visualization. CoRR abs/1506.06579 (2015). http://yosinski.com/deepvis
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Collet, P., Lutton, E., Schoenauer, M., Louchet, J.: Take it EASEA. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 891–901. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3 87
Varrette, S., Bouvry, P., Cartiaux, H., Georgatos, F.: Management of an academic HPC cluster: the UL experience. In: Proceedings of the 2014 International Conference on High Performance Computing & Simulation (HPCS 2014), Bologna, Italy, pp. 959–967. IEEE, July 2014. https://hpc.uni.lu
Simonyan, K., Zisserman, A.: 19-layer model from the arxiv paper: “very deep convolutional networks for large-scale image recognition”. Caffe Zoo/github gist (2014). https://gist.github.com/ksimonyan/3785162f95cd2d5fee77
Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. CoRR abs/1710.08864 (2017)
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427–436 (2015). https://www.cv-foundation.org/openaccess/content cvpr 2015/app/1A 047.pdf
