Patents by Inventor Stephanie Abrecht

Stephanie Abrecht 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: 11586855
    Abstract: A method is indicated for determining a confidence value of an object of a class detected in an input image with the aid of a trained neural network, including: producing an activation signature for the class of the detected object using a plurality of output images of a layer of the neural network, the input image being provided to the input of the neural network; scaling the activation signature to the dimension of the input image; comparing an object portion of the scaled activation signature with an activation signature distribution of all objects of the same class of a training data set of the neural network in order to determine the confidence value.
    Type: Grant
    Filed: June 22, 2020
    Date of Patent: February 21, 2023
    Assignee: Robert Bosch GmbH
    Inventors: Oliver Willers, Sebastian Sudholt, Shervin Raafatnia, Stephanie Abrecht
  • Patent number: 11531832
    Abstract: A method is described for determining a confidence value for an object of a class determined by a neural network in an input image. The method includes: preparing an activation signature with the aid of a multiplicity of output images of a layer of the neural network for the class of the object, with the input image being provided to the input of the neural network; scaling the activation signature to the size of the input image; comparing an overlapping area portion of an area of the activation signature with an area of an object frame in relation to the area of the activation signature in order to determine the confidence value.
    Type: Grant
    Filed: June 22, 2020
    Date of Patent: December 20, 2022
    Assignee: Robert Bosch GmbH
    Inventors: Stephanie Abrecht, Oliver Willers
  • Patent number: 11531899
    Abstract: A method for estimating a global uncertainty of output data of a computer implemented main neural network. The method includes determining a first measure quantifying to which extent the current input data of the main neural network is following the same distribution as the data, which was used for training the main neural network; generating a second measure quantifying the main neural network's certainty in its own prediction based on the input data; ascertaining a third measure, based on an estimation of class-discriminative features in the input data and a comparison of these features with a class activation probability distribution, especially wherein the class activation probability distribution was created based on estimated class-discriminative features during the training of the main neural network; and determining the global uncertainty based on at least two measures of the first, second and third measure.
    Type: Grant
    Filed: June 22, 2020
    Date of Patent: December 20, 2022
    Assignee: Robert Bosch GmbH
    Inventors: Oliver Willers, Sebastian Sudholt, Shervin Raafatnia, Stephanie Abrecht
  • Publication number: 20200410297
    Abstract: A method is indicated for determining a confidence value of an object of a class detected in an input image with the aid of a trained neural network, including: producing an activation signature for the class of the detected object using a plurality of output images of a layer of the neural network, the input image being provided to the input of the neural network; scaling the activation signature to the dimension of the input image; comparing an object portion of the scaled activation signature with an activation signature distribution of all objects of the same class of a training data set of the neural network in order to determine the confidence value.
    Type: Application
    Filed: June 22, 2020
    Publication date: December 31, 2020
    Inventors: Oliver Willers, Sebastian Sudholt, Shervin Raafatnia, Stephanie Abrecht
  • Publication number: 20200410364
    Abstract: A method for estimating a global uncertainty of output data of a computer implemented main neural network. The method includes determining a first measure quantifying to which extent the current input data of the main neural network is following the same distribution as the data, which was used for training the main neural network; generating a second measure quantifying the main neural network's certainty in its own prediction based on the input data; ascertaining a third measure, based on an estimation of class-discriminative features in the input data and a comparison of these features with a class activation probability distribution, especially wherein the class activation probability distribution was created based on estimated class-discriminative features during the training of the main neural network; and determining the global uncertainty based on at least two measures of the first, second and third measure.
    Type: Application
    Filed: June 22, 2020
    Publication date: December 31, 2020
    Inventors: Oliver Willers, Sebastian Sudholt, Shervin Raafatnia, Stephanie Abrecht
  • Publication number: 20200410282
    Abstract: A method is described for determining a confidence value for an object of a class determined by a neural network in an input image. The method includes: preparing an activation signature with the aid of a multiplicity of output images of a layer of the neural network for the class of the object, with the input image being provided to the input of the neural network; scaling the activation signature to the size of the input image; comparing an overlapping area portion of an area of the activation signature with an area of an object frame in relation to the area of the activation signature in order to determine the confidence value.
    Type: Application
    Filed: June 22, 2020
    Publication date: December 31, 2020
    Inventors: Stephanie Abrecht, Oliver Willers