Resumen de A Novel Continual Learning Approach for Competitive Neural Networks
Esteban José Palomo Ferrer, Juan Miguel Ortiz de Lazcano Lobato, David Fernández Rodríguez, Ezequiel López Rubio, María Maza
Continual learning tries to address the stability-plasticity dilemma to avoid catastrophic forgetting when dealing with non-stationary distributions. Prior works focused on supervised or reinforcement learning, but few works have considered continual learning for unsupervised learning methods. In this paper, a novel approach to provide continual learning for competitive neural networks is proposed. To this end, we have proposed a different learning rate function that can cope with non-stationary distributions by adapting the model to learn continuously. Experimental results performed with different synthetic images that change over time confirm the performance of our proposal.
