A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets

Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading to unstable and unreliable predictions. Autoencode...

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Bibliographic Details

Published in:	Sensors (Basel, Switzerland) Vol. 21; no. 22; p. 7731
Main Authors:	Pintelas, Emmanuel, Livieris, Ioannis E., Pintelas, Panagiotis E.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 20.11.2021 MDPI
Subjects:	Classification computer vision convolutional autoencoders convolutional neural networks Datasets Deep learning dimensionality reduction image classification Machine learning Noise Parameter estimation
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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