Patents by Inventor Vadim Sushko

Vadim Sushko 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).

  • Patent number: 12354343
    Abstract: A generative adversarial network. The generative adversarial network includes: a generator configured for generating an image and a corresponding label map; a discriminator configured for determining a classification of a provided image and a provided label map, wherein the classification characterizes whether the provided image and the provided label map have been generated by the generator or not and determining the classification comprises the steps of: determining a first feature map of the provided image; masking the first feature map according to the provided label map thereby determining a masked feature map; globally pooling the masked feature map thereby determining a feature representation of the provided image masked by the provided label map; determining a classification of the image based on the feature representation.
    Type: Grant
    Filed: July 19, 2022
    Date of Patent: July 8, 2025
    Assignee: ROBERT BOSCH GMBH
    Inventors: Anna Khoreva, Vadim Sushko, Dan Zhang
  • Patent number: 12272123
    Abstract: A method for training a generator for images from a semantic map that assigns to each pixel of the image a semantic meaning of an object to which this pixel belongs. The images generated by the generator and the at least one real training image that belong to the same semantic training map are supplied to a discriminator. The discriminator ascertains a semantic segmentation of the image assigned to it, the segmentation assigning a semantic meaning to each pixel of this image. From the semantic segmentation ascertained by the discriminator, it is evaluated whether the image supplied to the discriminator is a generated image or a real training image.
    Type: Grant
    Filed: August 20, 2021
    Date of Patent: April 8, 2025
    Assignee: ROBERT BOSCH GMBH
    Inventors: Anna Khoreva, Edgar Schoenfeld, Vadim Sushko, Dan Zhang
  • Patent number: 12266161
    Abstract: A computer-implemented method for training a generative adversarial network. A generator of the generative adversarial network is configured to generate at least one image based on at least one input value. Training the generative adversarial network includes maximizing a loss function that characterizes a difference between a first image determined by the generator for at least one first input value and a third image determined by the generator for at least one second input value.
    Type: Grant
    Filed: January 31, 2022
    Date of Patent: April 1, 2025
    Assignee: ROBERT BOSCH GMBH
    Inventors: Anna Khoreva, Vadim Sushko
  • Publication number: 20230267653
    Abstract: A method for generating images from a semantic map, which assigns to each pixel of the images a semantic meaning of an object to which this pixel belongs. The semantic map is provided as a map tensor comprising channels which each indicates all the pixels of the images to be generated, to which the semantic map assigns a specific semantic meaning; a set of variable pixels of the images to be generated is provided, which are to vary from one image to the next; using values taken from a random distribution, a noise tensor with channels is generated, those values of the noise tensor which relate to the set of variable pixels being reused for each image to be generated; the channels of the map tensor are merged with the channels of the noise tensor to yield an input tensor, which is mapped by a trained generator onto an image.
    Type: Application
    Filed: August 20, 2021
    Publication date: August 24, 2023
    Inventors: Anna Khoreva, Edgar Schoenfeld, Vadim Sushko
  • Publication number: 20230177809
    Abstract: The invention relates to a method (100) for training a generator (1) for images (3) from a semantic map (2, 5a) that assigns each pixel of the image (3) a semantic meaning (4) of an object to which that pixel belongs, wherein: a mixed image (6) is generated (140) from at least one image (3) generated by the generator (1) and at least one determined actual training image (5), in which mixed image a first genuine subset (6a) of pixels is occupied by relevant corresponding pixel values of the image (3) generated by the generator (1) and the remaining genuine subset (6b) of pixels is occupied by relevant corresponding pixel values of the actual training image (5); and the images (3) generated by the generator (1), the at least one actual training image (5), and at least one mixed image (6), which belong to the same semantic training map (5a), are supplied (150) to a discriminator (7), which is configured to distinguish images (3) generated by the generator (1) from actual images (5) of the scenery predefined by
    Type: Application
    Filed: August 20, 2021
    Publication date: June 8, 2023
    Inventors: Anna Khoreva, Edgar Schoenfeld, Vadim Sushko, Dan Zhang
  • Publication number: 20230134062
    Abstract: A method for training a generator for images from a semantic map that assigns to each pixel of the image a semantic meaning of an object to which this pixel belongs. The images generated by the generator and the at least one real training image that belong to the same semantic training map are supplied to a discriminator. The discriminator ascertains a semantic segmentation of the image assigned to it, the segmentation assigning a semantic meaning to each pixel of this image. From the semantic segmentation ascertained by the discriminator, it is evaluated whether the image supplied to the discriminator is a generated image or a real training image.
    Type: Application
    Filed: August 20, 2021
    Publication date: May 4, 2023
    Inventors: Anna Khoreva, Edgar Schoenfeld, Vadim Sushko, Dan Zhang
  • Publication number: 20230031755
    Abstract: A generative adversarial network. The generative adversarial network includes: a generator configured for generating an image and a corresponding label map; a discriminator configured for determining a classification of a provided image and a provided label map, wherein the classification characterizes whether the provided image and the provided label map have been generated by the generator or not and determining the classification comprises the steps of: determining a first feature map of the provided image; masking the first feature map according to the provided label map thereby determining a masked feature map; globally pooling the masked feature map thereby determining a feature representation of the provided image masked by the provided label map; determining a classification of the image based on the feature representation.
    Type: Application
    Filed: July 19, 2022
    Publication date: February 2, 2023
    Inventors: Anna Khoreva, Vadim Sushko, Dan Zhang
  • Publication number: 20220262106
    Abstract: A computer-implemented method for training a generative adversarial network. A generator of the generative adversarial network is configured to generate at least one image based on at least one input value. Training the generative adversarial network includes maximizing a loss function that characterizes a difference between a first image determined by the generator for at least one first input value and a third image determined by the generator for at least one second input value.
    Type: Application
    Filed: January 31, 2022
    Publication date: August 18, 2022
    Inventors: Anna Khoreva, Vadim Sushko
  • Publication number: 20220253702
    Abstract: A computer-implemented method for training a machine learning system, which includes a generator configured to generate at least one image. The method includes: generating, by the generator, a first image based on at least one randomly drawn value; determining, by a discriminator of the machine learning system, a first output characterizing two classifications of the first image and determining, by the discriminator, a second output characterizing two classifications of a provided second image; training the discriminator such that the content value and layout value in the first output characterize a classification into a first class and such that the content value and layout value in the second output characterize a classification into a second class; and training the generator such that the content value and layout value in the first output characterize a classification into the second class.
    Type: Application
    Filed: January 25, 2022
    Publication date: August 11, 2022
    Inventors: Anna Khoreva, Vadim Sushko