Abstract: A method for training an artificial neural network, in particular a Bayesian neural network, by way of training data sets, having a step of adapting the parameters of the artificial neural network depending on a loss function, the loss function encompassing a first term that represents an estimate of a lower bound of the distances between the classifications of the training data sets by the artificial neural network and the expected classifications of the training data sets. The loss function further encompasses a second term that is configured in such a way that differences in the aleatoric uncertainty in the training data sets over different samples of the artificial neural network are regulated.

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.

Abstract: A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.

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.

Abstract: A method for calculating a quality measure of a computer-implemented object detection algorithm, which may be used, in particular, for enabling the object detection algorithm for semi-automated, highly-automated or fully-automated robots. The method includes: assigning ascertained object detections to annotations, the object detections and/or the annotations corresponding to bounding boxes; determining deviations, in particular, distances of the annotations with respect to their assigned object detections; calculating the quality measure of the object detection algorithm based on the determined deviations, the quality measure representing a probability with which a deviation of an object detection from the annotation assigned to it exceeds or falls below a predefined threshold value.

Abstract: A method for training an artificial neural network, in particular a Bayesian neural network, by way of training data sets, having a step of adapting the parameters of the artificial neural network depending on a loss function, the loss function encompassing a first term that represents an estimate of a lower bound of the distances between the classifications of the training data sets by the artificial neural network and the expected classifications of the training data sets. The loss function further encompasses a second term that is configured in such a way that differences in the aleatoric uncertainty in the training data sets over different samples of the artificial neural network are regulated.

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.

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.