InGAN: Capturing and Retargeting the “DNA” of a Natural Image
Name
1812.00231.pdf
Description
Submitted version
Size
9.65 MB
Format
Adobe PDF
Checksum (MD5)
11b55b8806fecd7299165487f90ac642
Author(s)
Shocher, Assaf
Bagon, Shai
Isola, Phillip John
Irani, Michal
Date Issued
February 2020
Journal
Proceedings of the IEEE International Conference on Computer Vision
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shocher, Assaf et al. "InGAN: Capturing and Retargeting the “DNA” of a Natural Image." Proceedings of the IEEE International Conference on Computer Vision, October-November 2019, Seoul, South Korea, Institute of Electrical and Electronics Engineers, February 2020. © 2019 IEEE
Version
Original manuscript
Abstract
Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches. In this paper we propose an ''Internal GAN'' (InGAN) - an image-specific GAN - which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of significantly different sizes, shapes and aspect-ratios - all with the same internal patch-distribution (same ''DNA'') as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape. InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution. InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iccv.2019.00459
