Journal
SENSORS
Volume 21, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/s21227731
Keywords
convolutional autoencoders; dimensionality reduction; deep learning; convolutional neural networks; computer vision; image classification
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The study introduces a novel approach using a convolutional autoencoder topological model to address the issue of noise and redundant information affecting deep learning models, leading to a significant performance improvement by compressing and filtering initial high-dimensional input 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.
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