title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¿ÐµÑÐµÐ½Ð¾ÑÐ° ÑÑÐ¸Ð»Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð² Ð·Ð°Ð´Ð°ÑÐµ Makeup Transfer
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: Application of Generative Adversarial Neural Networks for Image Style Transfer in the Makeup Transfer Problem
Creators: name: ÐÐ°Ð±Ð´ÑÑÐµÐ², Ð ÑÑÐ°Ð½
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: 2023
Subjects: ÑÐ·ÑÐº Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÐ¸
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð°Ñ Ð³ÑÐ°ÑÐ¸ÐºÐ°
ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ
Ð¼ÐµÑÐ¾Ð´Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸
programming language Python
artificial neural networks
image transformation
generative adversarial networks
computer graphics
attention distribution
optimization methods

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ÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Makeup Transfer, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð½Ð¾Ð²Ð¾Ð¹ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð¸Ð· Ð½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ° Makeup Transfer Ð¾Ð´Ð½Ð¸Ð¼ Ð¸Ð· Ð²Ð°ÑÐ¸Ð°Ð½ÑÐ¾Ð² Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿ÐµÑÐµÐ½Ð¾ÑÐ° ÑÑÐ¸Ð»Ñ. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð¿Ð¾Ð¸ÑÐº ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¸ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿ÑÐ¾Ð´Ð²Ð¸Ð½ÑÑÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº ÑÑÐ¾Ð¹ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ðµ. ÐÑÐ» Ð²ÑÐ±ÑÐ°Ð½ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° ÐµÐ³Ð¾ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²ÐºÐ»ÑÑÐ°Ð»Ð° Ð² ÑÐµÐ±Ñ ÑÐ¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð½Ð¾Ð²Ð¾Ð³Ð¾ ÑÑÐµÐ½Ð¸ÑÐ¾Ð²Ð¾ÑÐ½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ, ÑÐ»ÑÑÑÐµÐ½Ð¸Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð¿ÑÐµÐ²Ð´Ð¾-Ð¿ÐµÑÐµÐ½Ð¾ÑÐ° Ð¼Ð°ÐºÐ¸ÑÐ¶Ð°, Ð° ÑÐ°ÐºÐ¶Ðµ Ð´Ð¾Ð±Ð°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼Ð° Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÐºÐ°ÑÑÑ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð´Ð»Ñ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð¿Ð¾ÑÐµÑÐ¸ Ð¼Ð°ÐºÐ¸ÑÐ¶Ð°. ÐÐ»Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ ÑÑÐµÑÐ° ÐºÐ°ÑÑÑ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ, Ð±ÑÐ»Ð° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð½Ð¾Ð²Ð°Ñ ÑÑÐ½ÐºÑÐ¸Ñ Ð¿Ð¾ÑÐµÑÐ¸ Fragmentation Spatial Profile Loss, ÐºÐ¾ÑÐ¾ÑÐ°Ñ ÑÐ²Ð»ÑÐµÑÑÑ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸ÐµÐ¹ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð¿Ð¾ÑÐµÑÐ¸, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð² Ð¾ÑÐ¸Ð³Ð¸Ð½Ð°Ð»Ðµ â Spatial Profile Loss. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð» ÑÐ»ÑÑÑÐ¸ÑÑ ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¿ÐµÑÐµÐ½Ð¾ÑÐ° Ð´Ð»Ñ Ð±Ð¾Ð»ÐµÐµ ÑÐ»Ð¾Ð¶Ð½Ð¾Ð³Ð¾ Ð¸ ÑÑÐºÐ¾Ð³Ð¾ Ð¼Ð°ÐºÐ¸ÑÐ¶Ð°, ÑÐ¼ÐµÐ½ÑÑÐ¸ÑÑ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð¿ÐµÑÐµÐ½Ð¾ÑÐ°, ÑÐ²ÑÐ·Ð°Ð½Ð½ÑÑ Ñ Ð½ÐµÐºÐ¾ÑÑÐµÐºÑÐ½ÑÐ¼ Ð¾ÑÐ²ÐµÑÐµÐ½Ð¸ÐµÐ¼, ÑÐ¼ÐµÐ½ÑÑÐ¸ÑÑ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸, ÑÑÐµÐ±ÑÐµÐ¼Ð¾Ð³Ð¾ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÐ¾Ð¼Ð¸Ð¼Ð¾ ÑÑÐ¾Ð³Ð¾, Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Telegram-Ð±Ð¾Ñ, ÑÐ²Ð»ÑÑÑÐ¸Ð¹ÑÑ Ð¸Ð½ÑÐµÑÑÐµÐ¹ÑÐ¾Ð¼ Ð´Ð»Ñ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ°ÑÐ¸Ð¸ ÑÐ°Ð±Ð¾ÑÑ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ½ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑ ÑÐµÑÑ Ð´Ð»Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð´Ð¾ÑÑÑÐ¿Ð¾Ð¼ Ñ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½ÑÑ ÑÑÑÑÐ¾Ð¹ÑÑÐ².
description: This work is devoted to the study of existing methods for solving the Makeup Transfer problem, as well as the development of a new modification of one of them. The Makeup Transfer task is one of the variants of the style transfer task. A search was made for novel and most advanced approaches to this problem. The most promising method based on the use of generative adversarial neural networks was chosen and its modification was developed. The development of the modification included compiling a new training data set, improving the generation of pseudo makeup transfer, and adding a mechanism for generating and applying an attention distribution map for the makeup loss function. To improve consideration of the attention distribution map, a new Fragmentation Spatial Profile Loss function was proposed, which is a modification of the loss function used in the original - Spatial Profile Loss. The developed method made it possible to improve the transfer quality for more complex and bright make-up, reduce the number of transfer defects associated with incorrect lighting, and reduce the amount of time required to train the model. In addition, a Telegram bot was developed, which is an interface for demonstrating the work of a trained network. It allows you to use the net for custom images that can be accessed from mobile devices.