Abstract: Computer-implemented method for determining an output signal based on an input signal and by means of a neural network. The neural network determines the output signal based on a layer output determined by a first layer of the neural network. The layer output is determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer.

Abstract: A device and a method of training a generative neural network. The method includes: generating an edge image using an edge detection applied to a digital image, the edge image comprising a plurality of edge pixels determined as representing edges of one or more digital objects in the digital image; selecting edge-pixels from the plurality of edge pixels; providing a segmentation image using the digital image, the segmentation image comprising a plurality of first pixels, the positions of the first pixels corresponding to the positions of the selected edge-pixels; selecting one or more second pixels for each first pixel in the segmentation image; generating a distorted segmentation image using a two-dimensional distortion applied to the segmentation image; and training the generative neural network using the distorted segmentation image as input image to estimate the digital image.

Abstract: A computer-implemented method for training a neural network including a neural ordinary differential equation (ODE) block. A first ODE solver may be used to train the neural ODE block. A second ODE solver may be used to train and verify that the neural ODE block describes an ODE as an ODE flow. During a forward pass of an iteration of training, a first performance value is obtained by applying the first ODE solver to the neural ODE block and a second performance value is obtained by applying the second ODE solver to the neural ODE block. An accuracy parameter of the first ODE solver is adjusted based on the difference between the first performance value and the second performance value.

Abstract: A method for operating a neural network is described comprising determining, for neural network input sensor data, neural network output data using the neural network, selecting a portion of output data points to form a region of interest and determining, for each of at least some output data points outside the region of interest, a contribution value representing a contribution of one or more input data points associated with the output data point for the neural network determining the output data point values assigned to output data points in the region of interest.