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Building efficient deep Hebbian networks for image classification tasks

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conference contribution
posted on 20.06.2017, 12:42 by Yanis Bahroun, Eugenie Hunsicker, Andrea Soltoggio
Multi-layer models of sparse coding (deep dictionary learning) and dimensionality reduction (PCANet) have shown promise as unsupervised learning models for image classification tasks. However, the pure implementations of these models have limited generalisation capabilities and high computational cost. This work introduces the Deep Hebbian Network (DHN), which combines the advantages of sparse coding, dimensionality reduction, and convolutional neural networks for learning features from images. Unlike in other deep neural networks, in this model, both the learning rules and neural architectures are derived from cost-function minimizations. Moreover, the DHN model can be trained online due to its Hebbian components. Different configurations of the DHN have been tested on scene and image classification tasks. Experiments show that the DHN model can automatically discover highly discriminative features directly from image pixels without using any data augmentation or semi-labeling.

History

School

  • Science

Department

  • Computer Science

Published in

International Conference on Artificial Neural Networks

Citation

BAHROUN, Y., HUNSICKER, E. and SOLTOGGIO, A., 2017. Building efficient deep Hebbian networks for image classification tasks. IN: Lintas, A. ...et al. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2017 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings, Part I. New York: Springer, pp. 364-372.

Publisher

© Springer

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

31/05/2017

Publication date

2017

Notes

This paper was presented at the 26th International Conference on Artificial Neural Networks, Alghero, Sardinia, 11-15th September. This is a pre-copyedited version of a contribution published in Lintas, A. ...et al. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2017 published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-68612-7.

ISBN

9783319686110

ISSN

0302-9743

Book series

Lecture Notes in Computer Science; 10614

Language

en

Location

Alghero, Italy

Exports