Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task
Neural and Evolutionary Computing
2017-11-29 v1 Machine Learning
Machine Learning
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
Cite
@article{arxiv.1711.10204,
title = {Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task},
author = {Guglielmo Montone and J. Kevin O'Regan and Alexander V. Terekhov},
journal= {arXiv preprint arXiv:1711.10204},
year = {2017}
}