Patents by Inventor Kaspar Sakmann

Kaspar Sakmann 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).

  • Publication number: 20220012549
    Abstract: A computer-implemented method of training an image classifier which uses any combination of labelled and/or unlabelled training images. The image classifier comprises a set of transformations between respective transformation inputs and transformation outputs. An inverse model is defined in which for a deterministic, non-injective transformation of the image classifier, its inverse is approximated by a stochastic inverse transformation. During training, for a given training image, a likelihood contribution for this transformation is determined based on a probability of its transformation inputs being generated by the stochastic inverse transformation given its transformation outputs. This likelihood contribution is used to determine a log-likelihood for the training image to be maximized (and its label, if the training image is labelled), based on which the model parameters are optimized.
    Type: Application
    Filed: June 11, 2021
    Publication date: January 13, 2022
    Inventors: Didrik Nielsen, Emiel Hoogeboom, Kaspar Sakmann, Max Welling, Priyank Jaini
  • Publication number: 20210357750
    Abstract: A system and method are provided for classifying objects in spatial data using a machine learned model, as well as a system and method for training the machine learned model. The machine learned model may comprise a content sensitive classifier, a location sensitive classifier and at least one outlier detector. Both classifiers may jointly distinguish between objects in spatial data being in-distribution or marginal-out-of-distribution. The outlier detection part may be trained on inlier examples from the training data, while the presence of actual outliers in the input data of the machine learnable model may be mimicked in the feature space of the machine learnable model during training. The combination of these parts may provide a more robust classification of objects in spatial data with respect to outliers, without having to increase the size of the training data.
    Type: Application
    Filed: April 19, 2021
    Publication date: November 18, 2021
    Inventors: Chaithanya Kumar Mummadi, Anna Khoreva, Kaspar Sakmann, Kilian Rambach, Piyapat Saranrittichai, Volker Fischer