RANDOM CLASSIFICATION MODEL HEAD FOR IMPROVED GENERALIZATION

A method comprising: receiving a primary training set comprising annotated data samples associated with one or more classes and annotated with class labels; constructing an auxiliary training set comprising at least some of the data samples, wherein each of the data samples is assigned at random to one of a set of identification classes, and annotated with an identification label associated with the identification class; at a training stage, train a machine learning model comprising a primary and auxiliary prediction heads, by: (i) training the primary prediction head on the primary training set to predict the class, and (ii) training the auxiliary prediction head on the auxiliary training set to predict the identification class, wherein an output layer of the machine learning model is configured to output a joint prediction which predicts the class label and is invariant to the identification label.