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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Vydané v:	Proceedings / IEEE Workshop on Applications of Computer Vision s. 889 - 898
Hlavní autori:	Saha, Surojit, Joshi, Sarang, Whitaker, Ross
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 26.02.2025
Predmet:	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
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