Abstract: Computer-implemented method for training a machine learning system. The machine learning system is configured to determine an output signal characterizing a likelihood of an input signal. The training includes: obtaining a first training input signal and a second training input signal, wherein the first training input signal characterizes an in-distribution signal and the second training input signal characterizes a contrastive signal; determining, by the machine learning system, a first output signal characterizing a likelihood of the first training input signal and determining, by the machine learning system, a second output signal characterizing a likelihood of the second training input signal; determining a loss value, wherein the loss value characterizes a difference between the first output signal and the second output signal; training the machine learning system based on the loss value.

Abstract: A computer-implemented method for training a neural network, wherein the neural network is an invertible neural network configured for accepting a sensor signal as input. The method includes: obtaining a first sensor signal from a dataset; sampling a value from a latent space of the neural network, wherein the value is sampled from a predefined hypervolume in the latent space; determining a second sensor signal from the sampled value by inversely mapping the sampled value through the neural network; determining a first latent representation by forward mapping the first sensor signal through the neural network; determining a second latent representation by forward mapping the second sensor signal through the neural network; determining a loss value from a loss function; and training the neural network based on a negative gradient of the loss value.

Abstract: A method for detecting anomalies in input image data, in particular from a camera, by detecting to what extent the input image data match at least one predefined distribution of image data or deviate from this predefined distribution. In the method: at least one transformation is provided, which maps input image data to data that have been information-reduced with regard to at least one aspect; at least one neural reconstruction network is provided, which is trained to reconstruct original image data from information-reduced data, which were obtained by applying the transformation to original image data from the predefined distribution; the input image data are mapped to information-reduced data by applying the transformation; the information-reduced data are mapped to reconstructed image data using the neural reconstruction network; the reconstructed image data are used to assess to what extent the input image data match or deviate from the predefined distribution.

Abstract: Computer-implemented method for training a machine learning system. The machine learning system is configured to determine an output signal characterizing a likelihood of an input signal. The training includes: obtaining a first training input signal and a second training input signal, wherein the first training input signal characterizes an in-distribution signal and the second training input signal characterizes a contrastive signal; determining, by the machine learning system, a first output signal characterizing a likelihood of the first training input signal and determining, by the machine learning system, a second output signal characterizing a likelihood of the second training input signal; determining a loss value, wherein the loss value characterizes a difference between the first output signal and the second output signal; training the machine learning system based on the loss value.

Abstract: A method for detecting anomalies in input image data, in particular from a camera, by detecting to what extent the input image data match at least one predefined distribution of image data or deviate from this predefined distribution. In the method: at least one transformation is provided, which maps input image data to data that have been information-reduced with regard to at least one aspect; at least one neural reconstruction network is provided, which is trained to reconstruct original image data from information-reduced data, which were obtained by applying the transformation to original image data from the predefined distribution; the input image data are mapped to information-reduced data by applying the transformation; the information-reduced data are mapped to reconstructed image data using the neural reconstruction network; the reconstructed image data are used to assess to what extent the input image data match or deviate from the predefined distribution.