Fraunhofer-gesellschaft Zur Foerderung Der Angewandten Forschung E.v.

Technische Universitaet Beriin

Sebastian Lapuschkin

Klaus-Robert Mueller

Wojciech Samek

Alexander Binder

Klaus-Robert Müller

Grégoire Montavon

Gregoire Montavon

Klaus-Robert Muller

The task of relevance score assignment to a set of items onto which an artificial neural network is applied is obtained by redistributing an initial relevance score derived from the network output, onto the set of items by reversely propagating the initial relevance score through the artificial neural network so as to obtain a relevance score for each item. In particular, this reverse propagation is applicable to a broader set of artificial neural networks and/or at lower computational efforts by performing same in a manner so that for each neuron, preliminarily redistributed relevance scores of a set of downstream neighbor neurons of the respective neuron are distributed on a set of upstream neighbor neurons of the respective neuron according to a distribution function.

Relevance score assignment for artificial neural networks

Growing community of inventors

Berlin, Germany

Sebastian Lapuschkin