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\begin{document}

\title{An Improved Uncertainty Autoencoder with Blurred Measurements}



\author[1]{Hongguang Xu}%
\author[2]{Ke Xu}%
\author[2]{Weiqiang Wu}%
\affil[1]{Harbin Institute of Technology Shenzhen}%
\affil[2]{Shenzhen Polytechnic}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\begin{abstract}
Compressed sensing (CS) techniques have enabled efficient acquisition
and recovery of sparse high-dimensional data via succinct
low-dimensional projections, which usually consist of an encoder and a
decoder. Unlike conventional CS techniques with the encoding-decoding
architecture, the uncertainty autoencoder (UAE) can sample from the
learned input data distribution without an explicit likelihood function,
hence avoids potential uninformative latent representations. However,
existing works on UAE mainly focus on the encoders and maximize the
lower bound of the mutual information between input and measurements,
rather than the decoders, which brings the shortcoming that the two may
not cope well. In this letter, we propose a novel training scheme for
UAE that blurs the measurements to learn the encoder and decoder
simultaneously. Experimental results show that the proposed method
improves the reconstruction performances when applied to UAE.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Electronics+Letters+MS+Word+Submission+Template.docx`  available at \url{https://authorea.com/users/563196/articles/632002-an-improved-uncertainty-autoencoder-with-blurred-measurements}

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