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Author: V. E. Antsiperov

Affiliation: Kotelnikov Institute of Radioengineering and Electronics of RAS, Mokhovaya 11-7, Moscow, Russian Federation

Keyword(s): Ideal Image, Counting Statistics, Autoencoders, Generative Model, Machine Learning, Feature Extraction.

Abstract: The article substantiates a generative model for autoencoders, learning by the input image representation based on a sample of random counts. This representation is used instead of the ideal image model, which usually involves too cumbersome descriptions of the source data. So, the reduction of the ideal image concept to sampling representations of fixed (controlled) size is one of the main goals of the article. It is shown that the corresponding statistical description of the sampling representation can be factorized into the product of the distributions of individual counts, which fits well into the naive Bayesian approach and some other machine learning procedures. Guided by that association the analogue of the well-known EM algorithm – the iterative partition–maximization procedure for generative autoencoders is synthesized. So, the second main goal of the article is to substantiate the partition–maximization procedure basing on the relation between autoencoder image restoration criteria and statistical maximum likelihood parameters estimation. We succeed this by modelling the input count probability distribution by the parameterized mixtures, considering the hidden mixture variables as autoencoder’s internal (coding) data. (More)

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Paper citation in several formats:
Antsiperov, V. (2022). Generative Model for Autoencoders Learning by Image Sampling Representations. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 354-361. DOI: 10.5220/0010915200003122

@conference{icpram22,
author={V. E. Antsiperov.},
title={Generative Model for Autoencoders Learning by Image Sampling Representations},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={354-361},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010915200003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Generative Model for Autoencoders Learning by Image Sampling Representations
SN - 978-989-758-549-4
IS - 2184-4313
AU - Antsiperov, V.
PY - 2022
SP - 354
EP - 361
DO - 10.5220/0010915200003122
PB - SciTePress