Machine learning

Accounting

Diagonal

Philosophy

Business

Artificial intelligence

Political science

MNIST database

Geometry

Consolidation (business)

Fisher information

Mathematics

Computer science

Linguistics

Artificial neural network

Unitary state

Forgetting

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.

Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting