Misc,
Functional Regularisation for Continual Learning
(2019)cite arxiv:1901.11356Comment: 12 pages, 2 figures.
Abstract
We introduce a framework for continual learning based on Bayesian inference
over the function space rather than the parameters of a deep neural network.
This method, referred to as functional regularisation for continual learning,
avoids forgetting a previous task by constructing and memorising an approximate
posterior belief over the underlying task-specific function. To achieve this we
rely on a Gaussian process obtained by treating the weights of the last layer
of a neural network as random and Gaussian distributed. Then, the training
algorithm sequentially encounters tasks and constructs posterior beliefs over
the task-specific functions by using inducing point sparse Gaussian process
methods. At each step a new task is first learnt and then a summary is
constructed consisting of (i) inducing inputs and (ii) a posterior distribution
over the function values at these inputs. This summary then regularises
learning of future tasks, through Kullback-Leibler regularisation terms, so
that catastrophic forgetting of earlier tasks is avoided. We demonstrate our
algorithm in classification datasets, such as Split-MNIST, Permuted-MNIST and
Omniglot.
