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Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
We present a coupled variational autoencoder (VAE) method, which improves the accuracy and robustness of the model representation of handwritten numeral images. The improvement is measured in both increasing the likelihood of the reconstructed images and in reducing divergence between the posterior...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947750/ https://www.ncbi.nlm.nih.gov/pubmed/35327933 http://dx.doi.org/10.3390/e24030423 |
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author | Cao, Shichen Li, Jingjing Nelson, Kenric P. Kon, Mark A. |
author_facet | Cao, Shichen Li, Jingjing Nelson, Kenric P. Kon, Mark A. |
author_sort | Cao, Shichen |
collection | PubMed |
description | We present a coupled variational autoencoder (VAE) method, which improves the accuracy and robustness of the model representation of handwritten numeral images. The improvement is measured in both increasing the likelihood of the reconstructed images and in reducing divergence between the posterior and a prior latent distribution. The new method weighs outlier samples with a higher penalty by generalizing the original evidence lower bound function using a coupled entropy function based on the principles of nonlinear statistical coupling. We evaluated the performance of the coupled VAE model using the Modified National Institute of Standards and Technology (MNIST) dataset and its corrupted modification C-MNIST. Histograms of the likelihood that the reconstruction matches the original image show that the coupled VAE improves the reconstruction and this improvement is more substantial when seeded with corrupted images. All five corruptions evaluated showed improvement. For instance, with the Gaussian corruption seed the accuracy improves by [Formula: see text] (from [Formula: see text] to [Formula: see text]) and robustness improves by [Formula: see text] (from [Formula: see text] to [Formula: see text]). Furthermore, the divergence between the posterior and prior distribution of the latent distribution is reduced. Thus, in contrast to the [Formula: see text]-VAE design, the coupled VAE algorithm improves model representation, rather than trading off the performance of the reconstruction and latent distribution divergence. |
format | Online Article Text |
id | pubmed-8947750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89477502022-03-25 Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder Cao, Shichen Li, Jingjing Nelson, Kenric P. Kon, Mark A. Entropy (Basel) Article We present a coupled variational autoencoder (VAE) method, which improves the accuracy and robustness of the model representation of handwritten numeral images. The improvement is measured in both increasing the likelihood of the reconstructed images and in reducing divergence between the posterior and a prior latent distribution. The new method weighs outlier samples with a higher penalty by generalizing the original evidence lower bound function using a coupled entropy function based on the principles of nonlinear statistical coupling. We evaluated the performance of the coupled VAE model using the Modified National Institute of Standards and Technology (MNIST) dataset and its corrupted modification C-MNIST. Histograms of the likelihood that the reconstruction matches the original image show that the coupled VAE improves the reconstruction and this improvement is more substantial when seeded with corrupted images. All five corruptions evaluated showed improvement. For instance, with the Gaussian corruption seed the accuracy improves by [Formula: see text] (from [Formula: see text] to [Formula: see text]) and robustness improves by [Formula: see text] (from [Formula: see text] to [Formula: see text]). Furthermore, the divergence between the posterior and prior distribution of the latent distribution is reduced. Thus, in contrast to the [Formula: see text]-VAE design, the coupled VAE algorithm improves model representation, rather than trading off the performance of the reconstruction and latent distribution divergence. MDPI 2022-03-18 /pmc/articles/PMC8947750/ /pubmed/35327933 http://dx.doi.org/10.3390/e24030423 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cao, Shichen Li, Jingjing Nelson, Kenric P. Kon, Mark A. Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder |
title | Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder |
title_full | Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder |
title_fullStr | Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder |
title_full_unstemmed | Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder |
title_short | Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder |
title_sort | coupled vae: improved accuracy and robustness of a variational autoencoder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947750/ https://www.ncbi.nlm.nih.gov/pubmed/35327933 http://dx.doi.org/10.3390/e24030423 |
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