  Neural networks are known to suffer from catastrophic forgetting when trained
on sequential datasets. While there have been numerous attempts to solve this
problem in large-scale supervised classification, little has been done to
overcome catastrophic forgetting in few-shot classification problems. We
demonstrate that the popular gradient-based model-agnostic meta-learning
algorithm (MAML) indeed suffers from catastrophic forgetting and introduce a
Bayesian online meta-learning framework that tackles this problem. Our
framework utilises Bayesian online learning and meta-learning along with
Laplace approximation and variational inference to overcome catastrophic
forgetting in few-shot classification problems. The experimental evaluations
demonstrate that our framework can effectively achieve this goal in comparison
with various baselines. As an additional utility, we also demonstrate
empirically that our framework is capable of meta-learning on sequentially
arriving few-shot tasks from a stationary task distribution.
