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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Detalles Bibliográficos
Autores principales: Pintelas, Emmanuel, Livieris, Ioannis E., Pintelas, Panagiotis E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8622369/
https://www.ncbi.nlm.nih.gov/pubmed/34833805
http://dx.doi.org/10.3390/s21227731

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8622369/
https://www.ncbi.nlm.nih.gov/pubmed/34833805
http://dx.doi.org/10.3390/s21227731

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