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Proceedings Paper

The filling-in function of the Bayesian AutoEncoder Network
Author(s): Kaneharu Nishino; Mary Inaba
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Paper Abstract

We developed the Bayesian AutoEncoder (BAE) to construct a multi-layer restricted Bayesian Network by extracting features from a training dataset. Networks constructed using BAE have hidden variables that represent features of the data and can execute inferences for each feature. In this paper, we show that a network constructed by BAE can not only recognize features but can also fill in lacking data. We performed experiments and confirmed this filling-in ability.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431E (19 June 2017); doi: 10.1117/12.2280931
Show Author Affiliations
Kaneharu Nishino, The Univ. of Tokyo (Japan)
Mary Inaba, The Univ. of Tokyo (Japan)

Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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