Review of Modern Neural Network Compression Methods for the Task of Processing Measurement Data

Abstract

In the article, the authors consider neural network methods of data compression, both lossy and lossless. The principles of operation and training of the following architectures are described in detail: transformer, recurrent neural networks with “long short-term memory”, bottleneck autoencoder, variational autoencoder and generative adversarial network. The efficiency of their work has been compared with classical compression algorithms and a conclusion has been made about the potential and prospects of their application.