Data Generation with Variational Autoencoders and Generative Adversarial Networks

Engineering Proceedings

article

Daniil Devyatkin¹  Ivan Trenev¹ 

[1] V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

关键词: machine learning;    deep learning;    autoencoders;    generative adversarial network;    MNIST;   

DOI  :  10.3390/engproc2023033037

来源: mdpi

【 摘 要 】

The paper considers the problem of modelling the distribution of data with noise in the input data. In this paper, we consider encoders and decoders, which solve the problem of modelling data distribution. The improvement of variational autoencoders (VAEs) is discussed. Practical implementation is performed using the Python programming language and the Keras framework. Generative adversarial networks (GANs) and VAEs with noisy data are demonstrated.

【 授权许可】

Unknown