[en] A computer-implemented method for training a neural network for classifying image data and a related computer program product are disclosed. A labelled input data set comprising a plurality of labelled image data samples is provided together with a neural network. The neural network comprises an input layer, at least one intermediate layer, and an output layer having one channel per label class. Each channel provides a mapping of labelled image data samples onto feature vectors. Furthermore, the input layer of a decoder network for reconstructing image data samples at its output is connecting the output layer of the neural network. A classifier predicts class labels as the labels of those channels for which a normed distance of its feature vector relative to a pre-determined reference point is smallest. A loss function for the neural network is suitable for steering, for each channel, the feature vectors onto which image data samples of the associated class are mapped, into a convex target region around the pre-determined reference point.
Research center :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège Telim
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Deliège, Adrien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Cioppa, Anthony ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications