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- Improving SeNA-CNN by Automating Task RecognitionPublication . Zacarias, Abel; Alexandre, LuísCatastrophic forgetting arises when a neural network is not capable of preserving the past learned task when learning a new task. There are already some methods proposed to mitigate this problem in arti cial neural networks. In this paper we propose to improve upon our previous state-of-the-art method, SeNA-CNN, such as to enable the automatic recognition in test time of the task to be solved and we experimentally show that it has excellent results. The experiments show the learning of up to 4 di erent tasks with a single network, without forgetting how to solve previous learned tasks.
- SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network AugmentationPublication . Zacarias, Abel; Alexandre, LuísLifelong 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.