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

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
Main Authors: Emmanuel Pintelas, Ioannis E. Livieris, Panagiotis E. Pintelas
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Sensors
Subjects:
convolutional autoencoders
dimensionality reduction
deep learning
convolutional neural networks
computer vision
image classification
Online Access:https://www.mdpi.com/1424-8220/21/22/7731

Internet

https://www.mdpi.com/1424-8220/21/22/7731

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