  Future deep learning systems call for techniques that can deal with the
evolving nature of temporal data and scarcity of annotations when new problems
occur. As a step towards this goal, we present FUSION (Few-shot UnSupervIsed
cONtinual learning), a learning strategy that enables a neural network to learn
quickly and continually on streams of unlabelled data and unbalanced tasks. The
objective is to maximise the knowledge extracted from the unlabelled data
stream (unsupervised), favor the forward transfer of previously learnt tasks
and features (continual) and exploit as much as possible the supervised
information when available (few-shot). The core of FUSION is MEML -
Meta-Example Meta-Learning - that consolidates a meta-representation through
the use of a self-attention mechanism during a single inner loop in the
meta-optimisation stage. To further enhance the capability of MEML to
generalise from few data, we extend it by creating various augmented surrogate
tasks and by optimising over the hardest. An extensive experimental evaluation
on public computer vision benchmarks shows that FUSION outperforms existing
state-of-the-art solutions both in the few-shot and continual learning
experimental settings.
