公开(公告)号：US20220156522A1

公开(公告)日：2022-05-19

申请号：US16951782

申请日：2020-11-18

Applicant: Adobe Inc.

Inventor： Elya SHECHTMAN ,  William PEEBLES ,  Richard ZHANG ,  Jun-Yan ZHU ,  Alyosha EFROS

IPC: G06K9/62 ,  G06T3/00 ,  G06N3/04 ,  G06N3/08

Abstract: Embodiments are disclosed for generative image congealing which provides an unsupervised learning technique that learns transformations of real data to improve the image quality of GANs trained using that image data. In particular, in one or more embodiments, the disclosed systems and methods comprise generating, by a spatial transformer network, an aligned real image for a real image from an unaligned real dataset, providing, by the spatial transformer network, the aligned real image to an adversarial discrimination network to determine if the aligned real image resembles aligned synthetic images generated by a generator network, and training, by a training manager, the spatial transformer network to learn updated transformations based on the determination of the adversarial discrimination network.