TY - GEN
T1 - Maintaining Natural Image Statistics with the Contextual Loss
AU - Mechrez, Roey
AU - Talmi, Itamar
AU - Shama, Firas
AU - Zelnik-Manor, Lihi
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images. When training CNNs, photorealism is usually attempted by adversarial training (GAN), that pushes the output images to lie on the manifold of natural images. GANs are very powerful, but not perfect. They are hard to train and the results still often suffer from artifacts. In this paper we propose a complementary approach, that could be applied with or without GAN, whose goal is to train a feed-forward CNN to maintain natural internal statistics. We look explicitly at the distribution of features in an image and train the network to generate images with natural feature distributions. Our approach reduces by orders of magnitude the number of images required for training and achieves state-of-the-art results on both single-image super-resolution, and high-resolution surface normal estimation. Project page: https://www.github.com/roimehrez/contextualLoss.
AB - Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images. When training CNNs, photorealism is usually attempted by adversarial training (GAN), that pushes the output images to lie on the manifold of natural images. GANs are very powerful, but not perfect. They are hard to train and the results still often suffer from artifacts. In this paper we propose a complementary approach, that could be applied with or without GAN, whose goal is to train a feed-forward CNN to maintain natural internal statistics. We look explicitly at the distribution of features in an image and train the network to generate images with natural feature distributions. Our approach reduces by orders of magnitude the number of images required for training and achieves state-of-the-art results on both single-image super-resolution, and high-resolution surface normal estimation. Project page: https://www.github.com/roimehrez/contextualLoss.
UR - https://www.scopus.com/pages/publications/85067227964
U2 - 10.1007/978-3-030-20893-6_27
DO - 10.1007/978-3-030-20893-6_27
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85067227964
SN - 9783030208929
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 427
EP - 443
BT - Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Li, Hongdong
A2 - Mori, Greg
A2 - Schindler, Konrad
A2 - Jawahar, C.V.
T2 - 14th Asian Conference on Computer Vision, ACCV 2018
Y2 - 2 December 2018 through 6 December 2018
ER -