Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task

Guglielmo Montone; J. Kevin O'Regan; Alexander V. Terekhov

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arXiv · 1711.10204

Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task

Guglielmo Montone·J. Kevin O'Regan·Alexander V. Terekhov

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Abstract

In the present work we propose a Deep Feed Forward network architecture which can be trained according to a sequential learning paradigm, where tasks of increasing difficulty are learned sequentially, yet avoiding catastrophic forgetting. The proposed architecture can re-use the features learned on previous tasks in a new task when the old tasks and the new one are related. The architecture needs fewer computational resources (neurons and connections) and less data for learning the new task than a network trained from scratch

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Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov. 2017-11-28. Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task. https://arxiv.org/abs/1711.10204

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