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A Novel Continual Learning Approach for Competitive Neural Networks

  • Esteban J. Palomo [1] Árbol académico ; Juan Miguel Ortiz-de-Lazcano-Lobato [1] Árbol académico ; José David Fernández-Rodríguez [1] ; Ezequiel López-Rubio [1] Árbol académico ; Rosa María Maza-Quiroga [1]
    1. [1] Universidad de Málaga

      Universidad de Málaga

      Málaga, España

  • Localización: Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II / José Manuel Ferrández Vicente (dir. congr.) Árbol académico, José Ramón Álvarez Sánchez (dir. congr.) Árbol académico, Félix de la Paz López (dir. congr.) Árbol académico, Hojjat Adeli (aut.), 2022, ISBN 978-3-031-06527-9, págs. 223-232
  • Idioma: inglés
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • 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.


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