ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders

The variational autoencoder (VAE) [19], [41] is a popular, deep, latent-variable model (DLVM) due to its simple yet effective formulation for modeling the data distribution. Moreover, optimizing the VAE objective function is more manageable than other DLVMs. The bottleneck dimension of the VAE is a...

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Bibliographic Details

Published in:	Proceedings / IEEE Workshop on Applications of Computer Vision pp. 889 - 898
Main Authors:	Saha, Surojit, Joshi, Sarang, Whitaker, Ross
Format:	Conference Proceeding
Language:	English
Published:	IEEE 26.02.2025
Subjects:	Autoencoders automatic relevancy detection Benchmark testing Computer architecture Computer vision Data models hierarchical prior Linear programming Measurement Neural networks Robustness Stability analysis variational autoencoders
ISSN:	2642-9381
Online Access:	Get full text
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