On the Use of Kullback–Leibler Divergence for Kernel Selection and Interpretation in Variational Autoencoders for Feature Creation

On the Use of Kullback–Leibler Divergence for Kernel Selection and Interpretation in Variational Autoencoders for Feature Creation

This study presents a novel approach for kernel selection based on Kullback–Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of latent variables to reduce the model’s paramet...

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Bibliographic Details
Main Authors: Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio G. Ravelo-García
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Information
Subjects:
convolutional neural network
feature selection
latent variables
probabilistic classifier
variational autoencoder
Online Access:https://www.mdpi.com/2078-2489/14/10/571

Internet

https://www.mdpi.com/2078-2489/14/10/571

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