RT Conference Proceedings
T1 A novel continual learning approach for competitive neural networks
A1 Fernández-Rodríguez, Jose David
A1 Maza Quiroga, Rosa María
A1 Palomo-Ferrer, Esteban José
A1 Ortiz-de-Lazcano-Lobato, Juan Miguel
A1 López-Rubio, Ezequiel
K1 Redes neuronales (Informática)
K1 Aprendizaje automático (Inteligencia artificial)
K1 Aprendizaje
AB 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.
YR 2022
FD 2022
LK https://hdl.handle.net/10630/24361
UL https://hdl.handle.net/10630/24361
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026