Neural style transfer: A review

Y **g, Y Yang, Z Feng, J Ye, Y Yu… - IEEE transactions on …, 2019‏ - ieeexplore.ieee.org
The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

Deep learning for image colorization: Current and future prospects

S Huang, X **, Q Jiang, L Liu - Engineering Applications of Artificial …, 2022‏ - Elsevier
Image colorization, as an essential problem in computer vision (CV), has attracted an
increasing amount of researchers attention in recent years, especially deep learning-based …

Stylediffusion: Controllable disentangled style transfer via diffusion models

Z Wang, L Zhao, W ** autoencoder for deep image manipulation
T Park, JY Zhu, O Wang, J Lu… - Advances in …, 2020‏ - proceedings.neurips.cc
Deep generative models have become increasingly effective at producing realistic images
from randomly sampled seeds, but using such models for controllable manipulation of …

[ספר][B] Rebooting AI: Building artificial intelligence we can trust

G Marcus, E Davis - 2019‏ - books.google.com
Two leaders in the field offer a compelling analysis of the current state of the art and reveal
the steps we must take to achieve a robust artificial intelligence that can make our lives …