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.

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.