Training a neural network model

Abstract

A system for training a neural network model, comprises a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to acquire training data, the training data comprising: data, an annotation for the data as determined by a user and auxiliary data, the auxiliary data describing at least one location of interest in the data, as considered by the user when determining the annotation for the data. The set of instructions when executed by the processor, further cause the processor to train the model using the training data, by minimising an auxiliary loss function that compares the at least one location of interest to an output of one or more layers of the model and minimising a main loss function that compares the annotation for the data as determined by the user to an annotation produced by the model.

Dependencies are inferred from the claim text.