title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½ÑÑ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: A fast-learning GAN model based on the WAE architectures
Creators: name: ÐÐµÐ½Ð¸ÑÐ¾Ð², ÐÐ°Ð²ÐµÐ»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÐ¸
Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑÑ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
generative adversarial networks
autoencoders
neural networks
learning time acceleration
image generation

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð½Ð¾Ð²Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ GAN, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐµÐ¹ Ñ ÑÐ¾ÑÑÐ°Ð½ÐµÐ½Ð¸ÐµÐ¼ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð³ÐµÐ½ÐµÑÐ¸ÑÑÐµÐ¼ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÑÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾ ÑÐ»ÑÑÑÐ¸ÑÑ Ð²ÑÐµÐ¼Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÐ½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑÐ¾Ð², Ð¸Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¸Ðº Ð¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. 2. ÐÑÐ±Ð¾Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ ÑÐµÑÐµÐ¹, ÑÐ¾ ÐµÑÑÑ ÐºÐ°Ðº Ð¼Ð¾Ð¶Ð½Ð¾ Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð¿Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. 3. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð¾Ð²Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ GAN. 4. Ð¢ÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹. ÐÑÐ½Ð¾Ð²Ð½Ð°Ñ Ð¸Ð´ÐµÑ Ð¼ÐµÑÐ¾Ð´Ð° â Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Â«Ð³ÐµÐ½ÐµÑÐ°ÑÐ¾ÑÐ°Â» Ð² Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´ÐµÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸ÐºÐ° Ð¸Ð· Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑÐ°. Ð¢ÐµÐ¼ ÑÐ°Ð¼ÑÐ¼, Ð²ÑÐµÐ¼Ñ Ð½Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ GAN Ð±ÑÐ´ÐµÑ ÑÐºÐ»Ð°Ð´ÑÐ²Ð°ÑÑÑÑ Ð¸Ð· Ð²ÑÐµÐ¼ÐµÐ½Ð¸, Ð¿Ð¾ÑÑÐ°ÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð½Ð° Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑÐ°, Ð¸ Ð²ÑÐµÐ¼ÐµÐ½Ð¸, Ð¿Ð¾ÑÑÐ°ÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð½Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Â«ÑÐ»Ð¸ÑÐ½Ð¸ÑÂ» GAN Ð¸ Ð´ÐµÐºÐ¾Ð´ÐµÑÐ°. ÐÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑÐ¾Ð², Ð¸Ð· ÐºÐ¾ÑÐ¾ÑÑÑ Ð±ÑÐ»Ð¸ Ð²ÑÐ±ÑÐ°Ð½Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ WGAN-GP Ð´Ð»Ñ GAN Ð¸ WAE Ð´Ð»Ñ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑÐ°. ÐÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð½Ð¾Ð²Ð°Ñ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹. Ð ÑÐ¾Ð´Ðµ ÐµÐµ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÑÑÐ´ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð½Ð° Ð´Ð°ÑÐ°ÑÐµÑÐµ MNIST, Ð¿Ð¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ ÐºÐ¾ÑÐ¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ñ, Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð¿Ð¾ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐµ, Ð½Ðµ ÑÑÑÑÐ¿Ð°Ð»Ð° Ð¿Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ñ Ð¸ÑÑÐ¾Ð´Ð½Ð¾Ð¼Ñ WGAN-GP, Ð¾Ð´Ð½Ð°ÐºÐ¾ Ð²ÑÐµÐ¼Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÐºÑÐ°ÑÐ¸Ð»Ð¾ÑÑ Ð½Ð° 48%.
description: This paper is devoted to the study of acceleration of training generative adversarial networks and the development of the new method of training GAN, which can significantly improve the learning time while maintaining the quality of the generated samples. The research set the following goals: 1. Analysis of existing models of generative adversarial networks and autoencoders, their architectures and methods of their training. 2. The choice of the most promising network models, that is, the most time-efficient training possible. 3. Development of a new GAN training methodology. 4. Testing the developed training methodology for generative adversarial networks. The main idea of the method is the use of a decoder from pre-trained autoencoder model as a generator in a generative adversarial network. Thus, the time spent on training the GAN will consist of the time spent on pre-training the autoencoder and the time spent on training merged GAN model. Various architectures of generative adversarial networks and autoencoders were examined, from which WGAN-GP architecture for GAN and WAE architecture for autoencoder were selected. A new methodology for training generative adversarial networks has been developed. During its methodology, a series of experiments have been conducted on MNIST dataset, according to the results of which the proposed model was not inferior in quality to the original WGAN-GP model, however, the training time has been reduced by 48%.