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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for assigning a relevance score to a set of items, the relevance score indicating a relevance with respect to an application of an artificial neural network composed of neurons onto the set of items so as to map the set of items onto a network output, the apparatus being configured to redistribute 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 acquire a relevance score for each item, wherein the apparatus is configured to perform the reverse propagation 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 to a set of upstream neighbor neurons of the respective neuron using a distribution function. 2. The apparatus according to claim 1 wherein the apparatus is configured such that the distribution function comprises a relevance conservation property. 3. The apparatus according to claim 1 wherein the apparatus is configured to perform the reverse propagation with equally using one distribution function for all neurons of the artificial neural network. 4. The apparatus according to claim 1 wherein the apparatus is configured such that the distribution function is a function of weights of the artificial neural network, determining a degree of influence of the respective neuron by the set of upstream neighbor neurons of the respective neuron, neuron activations of the set of upstream neighbor neurons as manifesting themselves upon the application of the artificial neural network onto the set of items, and a sum of preliminarily redistributed relevance scores of the set of downstream neighbor neurons of the respective neuron. 5. The apparatus according to claim 1 wherein the apparatus is configured such that, for each neuron j, the distribution function yielding how much relevance is redistributed as a relevance message Rij from the respective neuron j to the upstream neighbor neuron i is Rij=q(i)·m({Rjk,k is downstream neighbor neuron of j}) where m(K) with K being the number of downstream neighbors of the respective neuron j is a monotonically increasing function for all its components and yields the preliminarily redistributed relevance score Rj=m({Rjk,k is downstream neuron of j}) of the respective neuron j and q(i) is a function depending on weights wij connecting the upstream neighbor neuron i to the respective neuron j, an activation xi of upstream neighbor neuron i of the respective neuron j as resulting from the application of the artificial neural network onto the set of items, and a possibly zero-valued bias term bj of neuron j. 6. The apparatus according to claim 5 wherein m({Rjk,k is downstream neuron of j})=ΣkRjk. 7. The apparatus according to claim 5 wherein the apparatus is configured such that the function q(i) is a function p of weighted activations zij=s(xi,wij,bj) which are computed by a function s, so that q(i)=p({zij|i is upstream neighbor neuron of j}). 8. The apparatus according to claim 7 wherein function s is selected such that the weighted activation zij is given as zij=xiwij, or z ij = x i  w ij + b j I with I being the number of upstream neighbor neurons i of neuron j. 9. The apparatus according to claim 5 wherein the apparatus is configured such that the function q(i) satisfies, for each neuron j for which Rj>0, an ordering property, the ordering property being satisfied if a) if Σizij>0, then for all i1 and i2 being upstream neighbor neurons of neuron j for which zi1j<zi2j it holds true that q(i1)≦q(i2) b) OR for all i1 and i2 being upstream neighbor neurons of neuron j for which zi1j>0 and zi2j>0 and zi1j<zi2j Then it holds true that 0≦q(i1)≦q(i2) 10. The apparatus according to claim 5 wherein the apparatus is configured such that the function q(i) satisfies an ordering property, the ordering property being satisfied if for all i1 and i2 being upstream neighbor neurons of neuron j for which g(zi1j)<g(zi2j) it holds true that |q(i1)|≦|q(i2)| for a function g(•) that comprises its minimum at zero, and that is monotonically decreasing on the interval (−∞, 0) and monotonically increasing on the interval (0, +∞). 11. The apparatus according to claim 10 wherein the apparatus is configured such that the function g(•) is given as follows: g(z)=αmax(0,z)−βmin(0,z) with α>0,β≧0. 12. The apparatus according to claim 5, wherein the apparatus is configured such that the function q(i) inherits or is proportional to the Taylor decomposition of a neural network function of the neurons. 13. The apparatus according to claim 5, wherein the apparatus is configured such that the relevance message Rij is proportional to the Taylor decomposition of a function that is learnt from data and that maps activations xi of upstream neighbors i of a neuron j to the value m({Rjk, k is downstream neuron of j}) up to an approximation error. 14. The apparatus according to claim 1, wherein the apparatus is configured such that the distribution function is R ij = x i  w ij + b j n h  ( ∑ r   ( x r  w rj + b j n ) ) · m  ( { R jk , k   is   downstream   neighbor   neuron   of   j } ) Or R ij = x i  w ij h  ( ∑ r   ( x r  w rj + b j n ) ) · m  ( { R jk , k   is   downstream   neighbor   neuron   of   j } ) where n is the number of upstream neighbor neurons of the respective neuron j, Rij is the relevance message redistributed from the respective neuron j to the upstream neighbor neuron i and Rjk is the relevance message redistributed from the downstream neighbor neuron k to the respective neuron j, xi is the activation of upstream neighbor neuron i during the application of the neural network onto the set of items, wij is the weight connecting the upstream neighbor neuron i to the respective neuron j, wrj is also a weight connecting the upstream neighbor neuron r to the respective neuron j, and bj is a bias term of the respective neuron j, and h( ) is a scalar function, and where m(K) with K being the number of downstream neighbors of the respective neuron j is a monotonically increasing function for all its components and yields the preliminarily redistributed relevance score Rj=m({Rjk, k is downstream neuron of j}) of the respective neuron j. 15. The apparatus according to claim 1 wherein the apparatus is configured such that the distribution onto the set of upstream neighbor neurons i of the respective neuron j is performed using a distribution function, wherein the distribution function is R ij = ( α  ( x i  w ij + b j n ) + h  ( ∑ r   ( x r  w rj + b j n ) + ) - β  ( x i  w ij + b j n ) - h  ( ∑ r   ( x r  w rj + b j n ) - ) ) · m  ( { R jk , k   is   downstream   neighbor   neuron   of   j } ) Or R ij = ( α  ( x i  w ij + b j n ) + h  ( ( b j ) +  + ∑ r   ( x r  w rj ) + ) - β  ( x i  w ij + b j n ) - h  ( ( b j ) - + ∑ r   ( x r  w rj ) - ) ) · m  ( { R jk , k   is    downstream   neighbor   neuron   of    j } ) Where (z)+=max(0, z), (z)−=min(0, z), n is the number of upstream neighbor neurons of the respective neuron, Rij is the relevance message redistributed from the respective neuron j to the upstream neighbor neuron i and Rjk is the relevance message redistributed from the downstream neighbor neuron k to the respective neuron j, xi is the activation of upstream neighbor neuron i during the application of the neural network onto the set of items, wij is the weight connecting the upstream neighbor neuron i to the respective neuron j, wrj is also a weight connecting the upstream neighbor neuron r to the respective neuron j and bj is a bias term of the respective neuron j, and h( ) is a scalar function, and α>0, β≧0, α−β=1 and m(K) with K being the number of downstream neighbors of the respective neuron j is a monotonically increasing function for all its components and yields the preliminarily redistributed relevance score Rj=m({Rjk, k is downstream neuron of j}) of the respective neuron j. 16. The apparatus according to claim 14 wherein m({Rjk, k is downstream neuron of j})=ΣkRjk. 17. The apparatus according to claim 14 wherein h( ) is a stabilizing function h(t)=t+ε·sign(t). 18. The apparatus according to claim 1 wherein the apparatus is configured to compute, for each item i, the relevance scores Ri of the respective item i by summing up the relevance messages of neurons comprising the respective item as upstream neighbor neuron, redistributed to the respective item. 19. The apparatus according to claim 1 wherein the artificial neural network is directly applied onto the set of items so that the items of the set of items form upstream neighbors for a subset of the artificial neurons of the artificial neural network, and the network output corresponds to a neuron activation of a neuron at a downstream end of the artificial neural network. 20. The apparatus according to claim 1 wherein the network output is a scalar with the initial relevance score derived therefrom equaling, or being derived by applying a monotonically increasing function onto, a value of the scalar, or the network output is a vector with the initial relevance value equaling, or being derived by applying a monotonically increasing function onto, a value of one or more components of the vector. 21. The apparatus according to claim 1 wherein the apparatus is configured to perform the reverse propagation so that 0.95·R≦f(ΣRi)≦1.05·R with ΣRi denoting a sum over the relevance scores of all items i of the set of items and f being a monotonic function solely depending on ΣRi. 22. The apparatus according to claim 21 wherein the apparatus is configured to such that f is an identity function. 23. The apparatus according to claim 1 wherein the apparatus is configured such that for each neuron, a sum of relevance message values distributed to the set of upstream neighbor neurons of the respective neuron by the distribution function equals ξ(SN) or deviates therefrom by no more than 5% with SN denoting the sum of relevance messages from the set of downstream neighbor neurons of the respective neuron to the respective neuron and ξ denoting a monotonic function solely depending on SN. 24. The apparatus according to claim 23 wherein the apparatus is configured such that ξ is an identity function. 25. The apparatus according to claim 1 wherein the artificial neural network is layered so that each neuron belongs to one of a sequence of layers and the apparatus is configured to perform the reverse propagation with equally using one distribution function for all neurons of the artificial neural network. 26. The apparatus according to claim 1 wherein the artificial neural network is layered so that each neuron belongs to one of a sequence of layers and the apparatus is configured to perform the reverse propagation so that, for each layer, a sum of relevance message values distributed to the neurons of the respective layer equals ζ(SL) or deviates therefrom by no more than 5% with SL denoting the sum of preliminarily redistributed relevance scores of the neurons of a layer downstream to the respective layer and ζ denoting a monotonic function solely depending on SL. 27. The apparatus according to claim 1 wherein the set of items is, or is a combination of, a picture with each of the items of the set of items corresponding to one or more of the pixels or subpixels of the picture, and/or a video with each of the items of the set of items corresponding to one or more pixels or subpixels of pictures of the video, pictures of the video or picture sequences of the video, and/or an audio signal with each item of the set of items corresponding to one or more audio samples of the audio signal, and/or a feature map of local features or a transform locally or globally extracted from a picture, video or audio signal with the items of the set of items corresponding to local features, and/or a text with the items of the set of items corresponding to words, sentences or paragraphs of the text, and/or a graph such as a social network relations graph with the items of the set of items corresponding to nodes or edges or sets of nodes or a set of edges or subgraphs. 28. A system for data processing, comprising an apparatus for assigning a relevance score to a set of items according to claim 1, and an apparatus for processing of the set of items or data to be processed and derived from the set of items with adapting the processing depending on the relevance scores. 29. The system according to claim 28, wherein the processing is a lossy processing and the apparatus for processing is configured to decrease a lossiness of the lossy processing for items having higher relevance scores assigned therewith than compared to items having lower relevance scores assigned therewith. 30. The system according to claim 28, wherein the processing is a visualizing wherein the apparatus for adapting is configured to perform a highlighting in the visualization depending on the relevance scores. 31. The system according to claim 28, wherein the processing is a data replenishment by reading from memory or performing a further measurement wherein the apparatus for processing is configured to focus the data replenishment depending on the relevance scores. 32. A system for highlighting a region of interest, comprising an apparatus for assigning a relevance score to a set of items according to claim 1, and an apparatus for generating a relevance graph depending on the relevance scores. 33. A system for optimizing a neural network, comprising an apparatus for assigning a relevance score to a set of items according to claim 1; an apparatus for applying the apparatus for assigning onto a plurality of different sets of items; and an apparatus for detecting a portion of increased relevance within the neural network by accumulating relevances assigned to the neurons of the network during the application of the apparatus for assigning onto the plurality of different sets of items, and optimizing the artificial neural network depending on the portion of increased relevance. 34. A method for assigning a relevance score to a set of items, the relevance score indicating a relevance with respect to an application of an artificial neural network composed of neurons onto the set of items so as to map the set of items onto a network output, the apparatus being configured to 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 acquire a relevance score for each item, wherein the reverse propagation is performed 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 to a set of upstream neighbor neurons of the respective neuron using a distribution function. 35. A non-transitory digital storage medium having stored thereon a computer program for performing a method for assigning a relevance score to a set of items, the relevance score indicating a relevance with respect to an application of an artificial neural network composed of neurons onto the set of items so as to map the set of items onto a network output, the apparatus being configured to 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 acquire a relevance score for each item, wherein the reverse propagation is performed 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 to a set of upstream neighbor neurons of the respective neuron using a distribution function, when said computer program is run by a computer.