Patents by Inventor German Ros

German Ros 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).

  • Adversarial learning of photorealistic post-processing of simulation with privileged information
    Patent number: 10643320
    Abstract: Systems and method for generating photorealistic images include training a generative adversarial network (GAN) model by jointly learning a first generator, a first discriminator, and a set of predictors through an iterative process of optimizing a minimax objective. The first discriminator learns to determine a synthetic-to-real image from a real image. The first generator learns to generate the synthetic-to-real image from a synthetic image such that the first discriminator determines the synthetic-to-real image is real. The set of predictors learn to predict at least one of a semantic segmentation labeled data and a privileged information from the synthetic-to-real image based on at least one of a known semantic segmentation labeled data and a known privileged information corresponding to the synthetic image. Once trained, the GAN model may generate one or more photorealistic images using the trained GAN model.
    Type: Grant
    Filed: February 12, 2018
    Date of Patent: May 5, 2020
    Assignee: TOYOTA RESEARCH INSTITUTE, INC.
    Inventors: Kuan-Hui Lee, German Ros, Adrien D. Gaidon, Jie Li
  • ADVERSARIAL LEARNING OF PHOTOREALISTIC POST-PROCESSING OF SIMULATION WITH PRIVILEGED INFORMATION
    Publication number: 20190147582
    Abstract: Systems and method for generating photorealistic images include training a generative adversarial network (GAN) model by jointly learning a first generator, a first discriminator, and a set of predictors through an iterative process of optimizing a minimax objective. The first discriminator learns to determine a synthetic-to-real image from a real image. The first generator learns to generate the synthetic-to-real image from a synthetic image such that the first discriminator determines the synthetic-to-real image is real. The set of predictors learn to predict at least one of a semantic segmentation labeled data and a privileged information from the synthetic-to-real image based on at least one of a known semantic segmentation labeled data and a known privileged information corresponding to the synthetic image. Once trained, the GAN model may generate one or more photorealistic images using the trained GAN model.
    Type: Application
    Filed: February 12, 2018
    Publication date: May 16, 2019
    Applicant: Toyota Research Institute, Inc.
    Inventors: Kuan-Hui Lee, German Ros, Adrien D. Gaidon, Jie Li