Methods for training a neural network model for sequentially learning a plurality of domains associated with a task. At least one set of auxiliary model parameters is determined by simulating at least one first optimization step based on a set of current model parameters and at least one auxiliary domain associated with a primary domain comprising one or more data points. A set of primary model parameters is determined by performing a second optimization step based on the current model parameters and the primary domain and on the at least one set of auxiliary model parameters and the primary domain and/or the auxiliary domain. The model is updated with the set of primary model parameters.
METHOD AND SYSTEM FOR LEARNING REPRESENTATIONS LESS PRONE TO CATASTROPHIC FORGETTING