Open AccessManipulasi Gambar dengan Transfer Gaya Menggunakan Convolutional Neural Network
Author(s) -
Rakhmi Khalida,
Khairunnisa Fadhilla Ramdhania
Publication year - 2021
Publication title -
building of informatics, technology and science
Language(s) - English
Resource type - Journals
eISSN - 2685-3310
pISSN - 2684-8910
DOI - 10.47065/bits.v3i3.1049
Subject(s) - convolutional neural network , computer science , artificial intelligence , artificial neural network , transfer of learning , transfer (computing) , image (mathematics) , painting , power (physics) , computer vision , pattern recognition (psychology) , parallel computing , art , art history , physics , quantum mechanics
Recently computers have been able to produce photographs that allow users to compose selfies with van Gogh paintings. Inspired by the power of convolutional neural networks (CNN), he first learned how to use CNN to reproduce famous painting styles combined with self-portrait images. The method used is called a neural network transfer. However, early versions of neural networks had optimization problems, requiring hundreds or thousands of iterations to transfer forces combined with a single image. To overcome this in-efficiency, researchers developed the CNS-style PerStyle-Per-Model (PSPM) transfer method. The development of force transfer using a deep neural network is also called NST by training the VGG-16 model to change any image in one feed, foward propagation. A trained model can adjust to any drawing mode with just one iteration instead of thousands of iterations over the network and to get the most objective possible style of transfer