Spontaneous Concept Learning with Deep Autoencoder

DOI

10.2991/ijcis.2018.25905178How to use a DOI?

Keywords

artificial intelligence; machine learning; deep learning; unsupervised learning

Abstract

In this study we investigate information processing in deep neural network models. We demonstrate that unsupervised training of autoencoder models of certain class can result in emergence of compact and structured internal representation of the input data space that can be correlated with higher level categories. We propose and demonstrate practical possibility to detect and measure this emergent information structure by applying unsupervised clustering in the activation space of the focal hidden layer of the model. Based on our findings we propose a new approach to training neural network models based on emergent in unsupervised training information landscape, that is iterative, driven by the environment, requires minimal supervision and with intriguing similarities to learning of biologic systems. We demonstrate its viability with originally developed method of spontaneous concept learning that yields good classification results while learning new higher level concepts with very small amounts of supervised training data.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Serge Dolgikh
PY  - 2018
DA  - 2018/11/01
TI  - Spontaneous Concept Learning with Deep Autoencoder
JO  - International Journal of Computational Intelligence Systems
SP  - 1
EP  - 12
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.25905178
DO  - 10.2991/ijcis.2018.25905178
ID  - Dolgikh2018
ER  -