Patents by Inventor Amelie Royer

Amelie Royer has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Semantically-consistent image style transfer
    Patent number: 11380034
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for semantically-consistent image style transfer. One of the methods includes: receiving an input source domain image; processing the source domain image using one or more source domain low-level encoder neural network layers to generate a low-level representation; processing the low-level representation using one more high-level encoder neural network layers to generate an embedding of the input source domain image; processing the embedding using one or more high-level decoder neural network layers to generate a high-level feature representation of features of the input source domain image; and processing the high-level feature representation of the features of the input source domain image using one or more target domain low-level decoder neural network layers to generate an output target domain image that is from the target domain but that has similar semantics to the input source domain image.
    Type: Grant
    Filed: October 29, 2018
    Date of Patent: July 5, 2022
    Assignee: Google LLC
    Inventors: Stephan Gouws, Frederick Bertsch, Konstantinos Bousmalis, Amelie Royer, Kevin Patrick Murphy
  • SEMANTICALLY-CONSISTENT IMAGE STYLE TRANSFER
    Publication number: 20200342643
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for semantically-consistent image style transfer. One of the methods includes: receiving an input source domain image; processing the source domain image using one or more source domain low-level encoder neural network layers to generate a low-level representation; processing the low-level representation using one more high-level encoder neural network layers to generate an embedding of the input source domain image; processing the embedding using one or more high-level decoder neural network layers to generate a high-level feature representation of features of the input source domain image; and processing the high-level feature representation of the features of the input source domain image using one or more target domain low-level decoder neural network layers to generate an output target domain image that is from the target domain but that has similar semantics to the input source domain image.
    Type: Application
    Filed: October 29, 2018
    Publication date: October 29, 2020
    Inventors: Stephan Gouws, Frederick Bertsch, Konstantinos Bousmalis, Amelie Royer, Kevin Patrick Murphy