- 专利标题: Techniques for domain to domain projection using a generative model
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申请号: US17384357申请日: 2021-07-23
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公开(公告)号: US11880766B2公开(公告)日: 2024-01-23
- 发明人: Cameron Smith , Ratheesh Kalarot , Wei-An Lin , Richard Zhang , Niloy Mitra , Elya Shechtman , Shabnam Ghadar , Zhixin Shu , Yannick Hold-Geoffrey , Nathan Carr , Jingwan Lu , Oliver Wang , Jun-Yan Zhu
- 申请人: Adobe Inc.
- 申请人地址: US CA San Jose
- 专利权人: Adobe Inc.
- 当前专利权人: Adobe Inc.
- 当前专利权人地址: US CA San Jose
- 代理机构: Kilpatrick Townsend & Stockton LLP
- 主分类号: G06N3/08
- IPC分类号: G06N3/08 ; G06F3/04845 ; G06F3/04847 ; G06T11/60 ; G06T3/40 ; G06N20/20 ; G06T5/00 ; G06T5/20 ; G06T3/00 ; G06T11/00 ; G06F18/40 ; G06F18/211 ; G06F18/214 ; G06F18/21 ; G06N3/045
摘要:
An improved system architecture uses a pipeline including a Generative Adversarial Network (GAN) including a generator neural network and a discriminator neural network to generate an image. An input image in a first domain and information about a target domain are obtained. The domains correspond to image styles. An initial latent space representation of the input image is produced by encoding the input image. An initial output image is generated by processing the initial latent space representation with the generator neural network. Using the discriminator neural network, a score is computed indicating whether the initial output image is in the target domain. A loss is computed based on the computed score. The loss is minimized to compute an updated latent space representation. The updated latent space representation is processed with the generator neural network to generate an output image in the target domain.
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