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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2019

27.07.2018

Discriminative Autoencoder for Feature Extraction: Application to Character Recognition

verfasst von: Anupriya Gogna, Angshul Majumdar

Erschienen in: Neural Processing Letters | Ausgabe 3/2019

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Abstract

Conventionally, autoencoders are unsupervised representation learning tools. In this work, we propose a novel discriminative autoencoder. Use of supervised discriminative learning ensures that the learned representation is robust to variations commonly encountered in image datasets. Using the basic discriminating autoencoder as a unit, we build a stacked architecture aimed at extracting relevant representation from the training data. The efficiency of our feature extraction algorithm ensures a high classification accuracy with even simple classification schemes like KNN (K-nearest neighbor). We demonstrate the superiority of our model for representation learning by conducting experiments on standard datasets for character/image recognition and subsequent comparison with existing supervised deep architectures like class sparse stacked autoencoder and discriminative deep belief network.
Vorheriger Artikel Contractive Slab and Spike Convolutional Deep Belief Network

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Metadaten
Titel
Discriminative Autoencoder for Feature Extraction: Application to Character Recognition
verfasst von
Anupriya Gogna
Angshul Majumdar
Publikationsdatum
27.07.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9894-5

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