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 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 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 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 device for and computer implemented method of image content recognition and of training a neural network for image content recognition. The method comprising collecting a first set of digital images from a database, the first set of digital images is sampled from digital images assigned to a many shot class; creating a first training set comprising the collected first set of digital images; training a first artificial neural network comprising a first feature extractor and a first classifier for classifying digital images using the first training set; collecting first parameters of the trained first feature extractor, collecting second parameters of the trained classifier, determining third parameters of a second feature extractor of a second artificial neural network depending on the first parameters, determining fourth parameters of a second classifier for classifying digital images of the second artificial neural network.

Abstract: A device and computer implemented method for digital image content recognition. The method includes determining, depending on a digital image, a first candidate class for the content of the digital image by a baseline model neural network comprising a first feature extractor and a first classifier for classifying digital images; determining a second candidate class for the content of the digital image by a prototypical neural network comprising a second feature extractor and a second classifier for classifying digital images, classifying the content of the digital image into either the first candidate class or the second candidate class depending on the result of a comparison of a first confidence score for the first candidate class to a threshold or of a comparison of a first confidence score for the first candidate class to a second confidence score for the second candidate class.

Abstract: A computer-implemented method for training a classifier for classifying input signals provided to the classifier. The classifier is configured to obtain an output signal characterizing a classification of the input signal. The method for training includes: providing a set of perturbations; providing a subset of first training samples each comprising an input signal and a corresponding desired output signal from a first dataset of training samples; selecting a first perturbation for an input signal and a corresponding desired output signal from the subset; obtaining a second perturbation; obtaining a first adversarial example by applying the second perturbation to the input signal; adapting the classifier by training the classifier based on the first adversarial example and the corresponding desired output signal to harden the classifier against the second perturbation; replacing the first perturbation in the set of perturbations a linear combination of the first perturbation and the second perturbation.

Abstract: A computer-implemented method for training a classifier. The classifier is configured to classify input signals of digital image data and/or audio data. The training of the classifier is based on a perturbed input signal obtained by applying a perturbation provided from a plurality of perturbations to an input signal provided from a training dataset. The method includes: providing a plurality of initial perturbations; adapting a perturbation from the plurality of initial perturbations to an input signal, wherein the input signal is randomly drawn from the training dataset and the perturbation is adapted to the input signal such that applying the perturbation to the input signal yields a second input signal, which is classified differently than the first input signal; providing a subset of the plurality of initial perturbations as plurality of perturbations; and training the classifier based on the plurality of perturbations.

Abstract: A method for measuring the sensitivity of a classifier for digital images against adversarial attacks. The classifier includes at least one neural network. The method includes: providing a digital image for which the sensitivity is to be measured; providing a generator that is trained to map elements of a latent space to a realistic image; obtaining, according to a set of parameters, an element of the latent space; mapping, using the generator, this element to a disturbance in the space of realistic images; perturbing the digital image with this disturbance; determining, using the classifier, a classification result for the perturbed image; determining, from the classification result, the impact of the disturbance on the classification result; optimizing the set of parameters to maximize this impact; and determining, based at least in part on the maximum impact, the sensitivity of the classifier.

Abstract: A device and computer implemented method for digital image content recognition. The method includes determining, depending on a digital image, a first candidate class for the content of the digital image by a baseline model neural network comprising a first feature extractor and a first classifier for classifying digital images; determining a second candidate class for the content of the digital image by a prototypical neural network comprising a second feature extractor and a second classifier for classifying digital images, classifying the content of the digital image into either the first candidate class or the second candidate class depending on the result of a comparison of a first confidence score for the first candidate class to a threshold or of a comparison of a first confidence score for the first candidate class to a second confidence score for the second candidate class.

Abstract: A device for and computer implemented method of image content recognition and of training a neural network for image content recognition. The method comprising collecting a first set of digital images from a database, the first set of digital images is sampled from digital images assigned to a many shot class; creating a first training set comprising the collected first set of digital images; training a first artificial neural network comprising a first feature extractor and a first classifier for classifying digital images using the first training set; collecting first parameters of the trained first feature extractor, collecting second parameters of the trained classifier, determining third parameters of a second feature extractor of a second artificial neural network depending on the first parameters, determining fourth parameters of a second classifier for classifying digital images of the second artificial neural network.

Abstract: A method for executing a computational task in a data management system is provided. The method includes storing a first stored procedure in a first database management system (DBMS) including first data containers. The first stored procedure receives names of one or more of the first data containers to act as input or output data containers and includes first statements for triggering resolution of features of a respective first input or output data container. The method also includes storing a second stored procedure in a second DBMS including second data containers. The second stored procedure implements the computational task and operates on one or more of the second data containers. The method also includes receiving, by the first DBMS, a request from a client application to perform the computational task.

Abstract: A method for executing a computational task in a data management system is provided. The method includes storing a first stored procedure in a first database management system (DBMS) including first data containers. The first stored procedure receives names of one or more of the first data containers to act as input or output data containers and includes first statements for triggering resolution of features of a respective first input or output data container. The method also includes storing a second stored procedure in a second DBMS including second data containers. The second stored procedure implements the computational task and operates on one or more of the second data containers. The method also includes receiving, by the first DBMS, a request from a client application to perform the computational task.