  Methods proposed in the literature towards continual deep learning typically
operate in a task-based sequential learning setup. A sequence of tasks is
learned, one at a time, with all data of current task available but not of
previous or future tasks. Task boundaries and identities are known at all
times. This setup, however, is rarely encountered in practical applications.
Therefore we investigate how to transform continual learning to an online
setup. We develop a system that keeps on learning over time in a streaming
fashion, with data distributions gradually changing and without the notion of
separate tasks. To this end, we build on the work on Memory Aware Synapses, and
show how this method can be made online by providing a protocol to decide i)
when to update the importance weights, ii) which data to use to update them,
and iii) how to accumulate the importance weights at each update step.
Experimental results show the validity of the approach in the context of two
applications: (self-)supervised learning of a face recognition model by
watching soap series and learning a robot to avoid collisions.
