Abstract: A method for training a neural network.

Abstract: A device and computer-implemented method for determining a class of an element of an image in particular for operating a technical system. The method includes providing a first set of elements representing the image, providing a set of masks, determining a set of predictions for the class, and determining the class of the element depending on the set of predictions, wherein determining the set of predictions comprises determining a second set of elements representing the image depending on the first set of elements and a mask of the set of masks, wherein the mask indicates unmasked elements of the image and/or masked elements of the image, and determining a prediction for the set of predictions depending on the second set of elements.

Abstract: A computer-implemented method for training a classifier. The method includes: ascertaining a first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and a desired output signal characterizing a classification of the evaluation points is allocated to the first input signal; subdividing the first input signal into a plurality of second input signals according to an arrangement of the evaluation points; ascertaining a plurality of first representations, a first representation being ascertained for each second input signal of a first subset of the plurality of second input signals using the classifier; ascertaining an output signal using the classifier and based on the plurality of first representations, the output signal characterizing a classification of the first input signal; adapting at least one parameter of the classifier according to a loss value which characterizes a difference between the ascertained output signal and the desired output signal.

Abstract: A method for ascertaining an optimal architecture of an artificial neural network. The method includes: ascertaining the optimal architecture of the artificial neural network by repeatedly ascertaining a trajectory from the initial node to a terminal node based on the defined strategy, determining a reward for the ascertained trajectory, determining a cost function for the ascertained trajectory based on the ascertained reward for the trajectory and the flows associated with the edges along the trajectory, and respectively updating the flows associated with the edges along the trajectory, based on the cost function until an ascertained trajectory fulfills a termination criterion for the architecture search, wherein the trajectory that fulfills the termination criterion represents the optimal architecture.

Abstract: A method for operating a technical system and a technical system. The method includes providing for at least one class at least one class attribute comprising a description for members of the class, providing features characterizing a digital image, determining a class of the at least one class that classifies the digital image depending on the features, and determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image. The at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image. The method further includes operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute.

Abstract: A computer-implemented method for determining an adversarial perturbation for input signals, especially sensor signals or features of sensor signals, of a machine learning system. A best perturbation is determined iteratively, wherein the best perturbation is provided as adversarial perturbation after a predefined amount of iterations, wherein at least one iteration includes: sampling a perturbation; applying the sampled perturbation to an input signal thereby determining a potential adversarial example; determining an output signal from the machine learning system for the potential adversarial example, determining a loss value characterizing a deviation of the output signal to a desired output signal, wherein the desired output signal corresponds to the input signal, if the loss value is larger than a previous loss value setting the best perturbation to the sampled perturbation.

Abstract: A computer-implemented machine learning system. The machine learning system is configured to provide an output signal based on an input signal by forwarding the input signal through a plurality of layers of the machine learning system. At least one of the layers of the plurality of layers is configured to receive a layer input, which is based on the input signal, and to provide a layer output based on which the output signal is determined. The layer is configured to determine the layer output by means of a non-linear normalization of the layer input.

Abstract: A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.

Abstract: A computer-implemented method for determining an adversarial patch for a machine learning system. The machine learning system is configured for image analysis and determines an output signal based on an input image. The output signal is determined based on an output of an attention layer of the machine learning system. The adversarial patch is determined by optimizing the adversarial patch with respect to a loss function, wherein the loss function comprises a term that characterizes a sum of attention weights of the attention layer with respect to a position of the adversarial patch in the input image and the method comprises a step of maximizing the term.

Abstract: A method for automatically generating an artificial neural network that encompasses modules and connections that link those modules, successive modules and/or connections being added to a current starting network. Modules and/or connections that are to be added are selected randomly from a predefinable plurality of possible modules and connections that can be added. A plurality of possible refinements of the current starting network respectively are generated by adding to the starting network modules and/or connections that are to be added. One of the refinements from the plurality of possible refinements is then selected in order to serve as a current starting network in a subsequent execution of the method.

Abstract: A computer-implemented method for determining an output signal characterizing a first classification of an input image into a class from a plurality of classes. The output signal further characterizes a second classification of a robustness of the first classification against an attack with an adversarial patch.

Abstract: A method for training an automated learning system includes processing training input with a first neural network and processing the output of the first neural network with a second neural network. The input layer of the second neural network corresponding to the output layer of the first neural network. The output layer of the second neural network corresponding to the input layer of the first neural network. An objective function is determined using the output of the second neural network and a predetermined modification magnitude. The objective function is approximated using random Cauchy projections which are propagated through the second neural network.

Abstract: A computer-implemented method for determining an output signal characterizing a semantic segmentation and/or an instance segmentation of an image. The method includes: determining a first intermediate output signal from a machine learning system, wherein the first intermediate output signal characterizes a semantic segmentation and/or an instance segmentation of the image; adapting parameters of the machine learning system based on a loss function, wherein the loss function characterizes an entropy or a cross-entropy of the first intermediate output signal; determining the output signal from the machine learning system based on the image and the adapted parameters.

Abstract: A method for operating a detector that is set up to check whether a data signal that is supplied to a machine learning system has been manipulated. The machine learning system is first trained in adversarial fashion using a manipulated data signal, the manipulated data signal having been ascertained by manipulation of a training data signal, and the machine learning system being trained to provide in each case the same output signal when the training data signal or the manipulated data signal is supplied to it. The detector is trained using another manipulated data signal that is produced as a function of the trained machine learning system.

Abstract: A method for creating a machine learning system. The method includes: providing a directed graph including an input node and an output node, a probability being in each case assigned to each edge which characterizes the probability with which an edge is drawn. The probabilities are manipulated as a function of a characteristic degree of an exploration of the architectures of the directed graph prior to a random drawing of the architectures.

Abstract: A method for creating a machine learning system, which is configured for segmentation and object detection. The method includes: providing a directed graph, selecting a path through the graph, at least one additional node being selected from a subset and a path being selected through the graph from the input node along the edges via the additional node up to the output node, the path initially being drawn as a function of probabilities of the edges, which defines a drawing probability of all architectures within the graph, creating a machine learning system as a function of the selected path and training the created machine learning system.

Abstract: A computer-implemented method for adapting a pretrained machine learning system, which has been trained on a first training data set, to a second dataset, wherein the second dataset has different characteristics than the first data set. An input transformation module for partly undoing the distribution shift between the first and second training data set is provided.

Abstract: A system and method, in particular computer implemented method for determining a perturbation for attacking and/or validating an association tracker. The method includes providing digital image data that includes an object, determining with the digital image data a first feature that characterizes the object, providing in particular from a storage a second feature that characterizes a tracked object, determining the perturbation depending on a measure of a similarity between the first feature and the second feature.

Abstract: A method is described for measuring the vulnerability of an AI module to spoofing attempts, including the classification and/or regression onto which the AI module maps the update data set is ascertained as an unperturbed result for a predefined data set in the input space E; at least one perturbation S having a dimensionality d<D is applied to the predefined data set so that at least one perturbed data set results in the input space E; the classification and/or regression onto which the AI module maps the perturbed data set is ascertained as the perturbed result; the deviation of the perturbed result from the unperturbed result is ascertained using predefined metrics; in response to the deviation satisfying a predefined criterion, it is determined that the AI module with regard to the predefined data set is vulnerable to spoofing attempts having a dimensionality d.

Abstract: A device, computer-implemented method for the processing of digital sensor data and training methods therefor. A plurality of training tasks from a distribution of training tasks are provided, the training tasks characterizing the processing of digital sensor data. A parameter set for an architecture and for weights of an artificial neural network are determined with a first gradient-based learning algorithm and with a second gradient-based algorithm as a function of at least one first training task from the distribution of training tasks. The artificial neural network is trained with the first gradient-based learning algorithm as a function of the parameter set and as a function of a second training task.