Device and computer implemented method for training an artificial neural network

A device for processing data, comprising:

• an input for data;
• an output for an output signal;
• at least one processor;
• and
• a memory for an artificial neural network;
• wherein the device is configured to train the artificial neural network, the device being configured to:
• provide a neural network layer for an equivariant feature mapping having a plurality of output channels;
• group channels of the plurality of output channels of the neural network layer into a number of distinct groups, wherein the output channels of each individual distinct group of the number of distinct groups are organized into an individual grid defining a spatial location of each of the output channels of the individual distinct group in the grid for the individual distinct group;
• provide, for each of the output channels of each individual distinct group of the number of distinct groups, a distinct normalization function, wherein the distinct normalization function is defined depending on the spatial location of the output channel in the grid in that the output channel is organized, and depending on tunable hyperparameters for the normalization function;
• determine an output of the artificial neural network depending on a result of each of the distinct normalization functions;
• and
• train the hyperparameters in a training of the artificial neural network;
• wherein the provision of the distinct normalization function, includes providing, for each of the output channels of each individual distinct group of the number of distinct groups, a distinct normalization function, a neighborhood function defining a set of output channels, wherein the normalization function for the output channel is defined depending on output channels of the neural network layer in the set of output channels and disregarding other output channels.

A non-transitory computer readable storage medium on which is stores computer program for training an artificial neural network, the computer program, when executed by a computer, causing the computer to perform the following steps:

• providing a neural network layer for an equivariant feature mapping having a plurality of output channels;
• grouping channels of the plurality of output channels of the neural network layer into a number of distinct groups, wherein the output channels of each individual distinct group of the number of distinct groups are organized into an individual grid defining a spatial location of each of the output channels of the individual distinct group in the grid for the individual distinct group;
• providing, for each of the output channels of each individual distinct group of the number of distinct groups, a distinct normalization function, wherein the distinct normalization function is defined depending on the spatial location of the output channel in the grid in that the output channel is organized, and depending on tunable hyperparameters for the normalization function;
• determining an output of the artificial neural network depending on a result of each of the distinct normalization functions;
• and
• training the hyperparameters in a training of the artificial neural network;
• wherein the providing of the distinct normalization function, includes providing, for each of the output channels of each individual distinct group of the number of distinct groups, a distinct normalization function, a neighborhood function defining a set of output channels, wherein the normalization function for the output channel is defined depending on output channels of the neural network layer in the set of output channels and disregarding other output channels.