Abstract: A computer-implemented method for classifying an input signal that is ascertained as a function of an output signal of a sensor as to whether or not it has an anomaly, it being decided, as a function of an output signal of an autoencoder (60) to which the input signal is supplied, whether or not the input signal has the anomaly. The autoencoder has at least one encoder and at least one decoder. An intermediate variable is ascertained as a function of the input signal by the encoder, and the output signal is ascertained by the decoder as a function of the intermediate variable. The autoencoder provides, for the input signal supplied to it, a plurality of hypotheses of formula for the reconstruction of the input signal, and the output signal is ascertained as a function of this plurality of hypotheses of formula.

Abstract: The invention provides a method for recognition of information in digital image data, said method comprising a learning phase on a data set of example digital images having known information, and characteristics of categories are computed automatically from each example digital image and compared to its known category, said method comprises training a convolutional neural network comprising network parameters using said data set, in which via deep learning each layer of said convolutional neural network is represented by a linear decomposition of all filters as learned in each layer into basis functions.

Abstract: The invention provides a method for recognition of information in digital image data, said method comprising a learning phase on a data set of example digital images having known information, and characteristics of categories are computed automatically from each example digital image and compared to its known category, said method comprises training a convolutional neural network comprising network parameters using said data set, in which via deep learning each layer of said convolutional neural network is represented by a linear decomposition of all filters as learned in each layer into basis functions.

Abstract: The invention provides a method for recognition of information in digital image data, said method comprising a learning phase on a data set of example digital images having known information, and characteristics of categories are computed automatically from each example digital image and compared to its known category, said method comprises training a convolutional neural network comprising network parameters using said data set, in which via deep learning each layer of said convolutional neural network is represented by a linear decomposition of all filters as learned in each layer into basis functions.