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1.发明申请公开(公告)号：US20210264235A1
公开(公告)日：2021-08-26
申请号：US16798271
申请日：2020-02-21
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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2.发明授权公开(公告)号：US11468294B2
公开(公告)日：2022-10-11
申请号：US16798271
申请日：2020-02-21
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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3.发明申请公开(公告)号：US20220414431A1
公开(公告)日：2022-12-29
申请号：US17899936
申请日：2022-08-31
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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4.发明授权公开(公告)号：US11615292B2
公开(公告)日：2023-03-28
申请号：US17899936
申请日：2022-08-31
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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