公开(公告)号：US12249132B2

公开(公告)日：2025-03-11

申请号：US17815451

申请日：2022-07-27

Applicant: Adobe Inc.

Inventor： Yijun Li ,  Nicholas Kolkin ,  Jingwan Lu ,  Elya Shechtman

IPC: G06V10/82 ,  G06V10/46 ,  G06V10/77

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.

公开(公告)号：US20240037922A1

公开(公告)日：2024-02-01

申请号：US17815451

申请日：2022-07-27

Applicant: Adobe Inc.

Inventor： Yijun Li ,  Nicholas Kolkin ,  Jingwan Lu ,  Elya Shechtman

IPC: G06V10/82 ,  G06V10/77 ,  G06V10/46

CPC classification number: G06V10/82 ,  G06V10/7715 ,  G06V10/469

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for adapting generative neural networks to target domains utilizing an image translation neural network. In particular, in one or more embodiments, the disclosed systems utilize an image translation neural network to translate target results to a source domain for input in target neural network adaptation. For instance, in some embodiments, the disclosed systems compare a translated target result with a source result from a pretrained source generative neural network to adjust parameters of a target generative neural network to produce results corresponding in features to source results and corresponding in style to the target domain.