In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting.
A method applicable for a class-incremental problem was presented in REF , where EWC is combined with a reparametrization of the network in form of a factorized rotation what results in a better performance on previous tasks.
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
2018 24th International Conference on Pattern Recognition (ICPR)
2018 24th International Conference on Pattern Recognition (ICPR)
Computer Science