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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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 21; H. 22; S. 7731
Hauptverfasser:	Pintelas, Emmanuel, Livieris, Ioannis E., Pintelas, Panagiotis E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 20.11.2021 MDPI
Schlagworte:	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
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Abstract	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. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models’ vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.
AbstractList	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. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models' vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.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. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models' vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images. 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. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models’ vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.
Author	Livieris, Ioannis E. Pintelas, Emmanuel Pintelas, Panagiotis E.
AuthorAffiliation	2 Core Innovation and Technology O.E., 11745 Athens, Greece; livieris@upatras.gr 1 Department of Mathematics, University of Patras, 26500 Patras, Greece; pintelas@math.upatras.gr
AuthorAffiliation_xml	– name: 1 Department of Mathematics, University of Patras, 26500 Patras, Greece; pintelas@math.upatras.gr – name: 2 Core Innovation and Technology O.E., 11745 Athens, Greece; livieris@upatras.gr
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SubjectTerms	Classification computer vision convolutional autoencoders convolutional neural networks Datasets Deep learning dimensionality reduction image classification Machine learning Noise Parameter estimation
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Title	A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets
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