METHOD FOR TRAINING A CLASSIFICATION MODEL USING CLASS PROTOTYPES OF PREVIOUS CLASSES, AND CORRESPONDING SYSTEM

A method for training a classification model (f) with a training dataset (X,Y) and a set of class prototypes (pc), the method comprising an initialization of a class prototype for at least one class of the training dataset not having a class prototype in the set of class prototypes, and wherein training is performed using a loss function including:- a first component (LSC) to learn, by the classification model, representation of samples of the training dataset,- a second component (Lp) configured to update the at least one initialized class prototype by optimizing a similarity between the initialized class prototype and the output of the classification model,- a third component (Ld) configured to update at least one class prototype of the set of class prototypes and the classification model by preserving, during training, an order distances.