Generative Model of Autoencoders Self-Learning on Images Represented by Count Samples

The paper substantiates the concept of autoencoders focused on automatic generation of compressed images. We propose a solution to the problem of synthesizing such autoencoders in the context of machine learning methods, understood here as learning based on the input images themselves (in the bootst...

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Bibliographic Details
Published inAutomation and remote control Vol. 83; no. 12; pp. 1959 - 1983
Main Author Antsiperov, V. E.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.12.2022
Springer Nature B.V
Subjects
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Computer-Aided Engineering (CAD
Control
Machine learning
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Mixtures
Representations
Robotics
Sampling
Systems Theory
Thematic Issue
mixture of distributions
receptive field
autoencoder
generative model
machine learning
EM algorithm
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Summary:The paper substantiates the concept of autoencoders focused on automatic generation of compressed images. We propose a solution to the problem of synthesizing such autoencoders in the context of machine learning methods, understood here as learning based on the input images themselves (in the bootstrap spirit). For these purposes, a special representation of images has been developed using samples of counts of a controlled size (sampling representations). Based on the specifics of this representation, a generative model of autoencoders is formalized, which is then specified to a probabilistic parametric sampling model in the form of a mixture of components. Based on the concept of receptive fields, a reduction of the general model of a mixture of components to a grid model of finite components of an exponential family is discussed. This allows the synthesis of computationally realistic coding algorithms.
ISSN:0005-1179
1608-3032
DOI:10.1134/S00051179220120098