  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.
