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Non-parametric e-mixture of Density Functions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Semi Supervised Autoencoder

Authors : Anupriya Gogna, Angshul Majumdar

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Autoencoders are self-supervised learning tools, but are unsupervised in the sense that class information is not required for training; but almost invariably they are used for supervised classification tasks. We propose to learn the autoencoder for a semi-supervised paradigm, i.e. with both labeled and unlabeled samples available. Given labeled and unlabeled data, our proposed autoencoder automatically adjusts – for unlabeled data it acts as a standard autoencoder (unsupervised) and for labeled data it additionally learns a linear classifier. We use our proposed semi-supervised autoencoder to (greedily) construct a stacked architecture. We demonstrate the efficacy our design in terms of both accuracy and run time requirements for the case of image classification. Our model is able to provide high classification accuracy with even simple classification schemes as compared to existing models for deep architectures.

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Metadata
Title
Semi Supervised Autoencoder
Authors
Anupriya Gogna
Angshul Majumdar
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46671-2

Electronic ISBN: 978-3-319-46672-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46672-9_10

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