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2016 | OriginalPaper | Chapter

Investigation of the Efficiency of Unsupervised Learning for Multi-task Classification in Convolutional Neural Network

Authors : Jonghong Kim, Gil-Jin Jang, Minho Lee

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

In this paper, we analyze the efficiency of unsupervised learning features in multi-task classification, where the unsupervised learning is used as initialization of Convolutional Neural Network (CNN) which is trained by a supervised learning for multi-task classification. The proposed method is based on Convolution Auto Encoder (CAE), which maintains the original structure of the target model including pooling layers for the proper comparison with supervised learning case. Experimental results show the efficiency of the proposed feature extraction method based on unsupervised learning in multi-task classification related with facial information. The unsupervised learning can produce more discriminative features than those by supervised learning for multi-task classification.

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previous chapter An Architecture Design Method of Deep Convolutional Neural Network
next chapter Sparse Auto-encoder with Smoothed Regularization
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Metadata
Title
Investigation of the Efficiency of Unsupervised Learning for Multi-task Classification in Convolutional Neural Network
Authors
Jonghong Kim
Gil-Jin Jang
Minho Lee
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46674-3

Electronic ISBN: 978-3-319-46675-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46675-0_60

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