公开(公告)号：US20240135672A1

公开(公告)日：2024-04-25

申请号：US17971169

申请日：2022-10-20

Applicant: Adobe Inc.

Inventor： Yijun Li ,  Zhixin Shu ,  Zhen Zhu ,  Krishna Kumar Singh

IPC: G06V10/70 ,  G06N3/04 ,  G06T11/00 ,  G06T15/08

CPC classification number: G06V10/70 ,  G06N3/0454 ,  G06T11/001 ,  G06T15/08

Abstract: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.

公开(公告)号：US20240233318A9

公开(公告)日：2024-07-11

申请号：US17971169

申请日：2022-10-21

Applicant: Adobe Inc.

Inventor： Yijun Li ,  Zhixin Shu ,  Zhen Zhu ,  Krishna Kumar Singh

IPC: G06V10/70 ,  G06N3/04 ,  G06T11/00 ,  G06T15/08

CPC classification number: G06V10/70 ,  G06N3/0454 ,  G06T11/001 ,  G06T15/08

Abstract: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.