  While neural networks are powerful function approximators, they suffer from
catastrophic forgetting when the data distribution is not stationary. One
particular formalism that studies learning under non-stationary distribution is
provided by continual learning, where the non-stationarity is imposed by a
sequence of distinct tasks. Most methods in this space assume, however, the
knowledge of task boundaries, and focus on alleviating catastrophic forgetting.
In this work, we depart from this view and move the focus towards faster
remembering -- i.e measuring how quickly the network recovers performance
rather than measuring the network's performance without any adaptation. We
argue that in many settings this can be more effective and that it opens the
door to combining meta-learning and continual learning techniques, leveraging
their complementary advantages. We propose a framework specific for the
scenario where no information about task boundaries or task identity is given.
It relies on a separation of concerns into what task is being solved and how
the task should be solved. This framework is implemented by differentiating
task specific parameters from task agnostic parameters, where the latter are
optimized in a continual meta learning fashion, without access to multiple
tasks at the same time. We showcase this framework in a supervised learning
scenario and discuss the implication of the proposed formalism.
