Method for estimating a global uncertainty of a neural network

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United States of America Patent

PATENT NO 11531899
APP PUB NO 20200410364A1
SERIAL NO

16907957

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Abstract

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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.

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Patent Owner(s)

  • ROBERT BOSCH GMBH

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Abrecht, Stephanie Stuttgart, DE 3 7
Raafatnia, Shervin Gerlingen, DE 2 6
Sudholt, Sebastian Neuenstadt am Kocher, DE 5 7
Willers, Oliver Korb, DE 11 9

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