METHOD AND DEVICE FOR TRAINING A CLASSIFICATION MODEL

The invention concerns a computer-implemented method for training a classification model, said method comprising the steps of:- obtaining (S10) a classification model comprising a representation backbone (320) configured to generate a representation of input samples and to group the input samples into clusters according to a similarity criteria of the representations associated to the input samples, the classification model further comprising a linear classifier (330) configured for assigning a vector P1 to a cluster, each component P1[k] of the vector P1 corresponding to an estimate of the probability of the cluster belonging to a class c[k], k ranging from 1 to K;- jointly training (S20) the representation backbone and the linear classifier by minimizing a loss function which depends on parameters of the representation backbone and weights of the linear classifier; and,- updating (S30) parameters of the representation backbone and weights of the linear classifier, so as to obtain an updated classification model.