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Orientador(es)
Resumo(s)
Lifelong learning aims to develop machine learning systems
that can learn new tasks while preserving the performance on previous
learned tasks. In this paper we present a method to overcome catastrophic
forgetting on convolutional neural networks, that learns new
tasks and preserves the performance on old tasks without accessing the
data of the original model, by selective network augmentation. The experiment
results showed that SeNA-CNN, in some scenarios, outperforms
the state-of-art Learning without Forgetting algorithm. Results
also showed that in some situations it is better to use SeNA-CNN instead
of training a neural network using isolated learning.
Descrição
Palavras-chave
Lifelong Learning Catastrophic Forgetting Convolutional Neural Networks Supervised Learning