APPROACHES TO SOLVING THE PROBLEM OF CREATING ONE-SHOT LEARNING METHODS OF LEARNING ARTIFICIAL NEURAL NETWORKS
The article examines the analysis of existing developments, methods and approaches for solving the problem of creating one-shot learning methods for training artificial neural networks. Methods of one-shot training or training in several stages (one-shot learning) is a paradigm that aims at a much faster and effective teaching. The goal of such a system is to learn to classify data from a single sample or from a very small training dataset, just as the human brain is often able to recognize an object by seeing it only once or twice. The article proposes a solution to this problem by identifying the semantics of input, output and intermediate data. The research results will allow for a sharp increase in the number of effective solutions to machine learning problems in various areas, for which it was previously impossible to create large datasets and / or training of which was not possible due to high requirements for the speed of computing technology. In addition, it is assumed that the developed mechanisms will significantly improve the accuracy of solving the entire class of machine learning problems