  It is an important yet challenging setting to continually learn new tasks
from a few examples. Although numerous efforts have been devoted to either
continual learning or few-shot learning, little work has considered this new
setting of few-shot continual learning (FSCL), which needs to minimize the
catastrophic forgetting to the old tasks and gradually improve the ability of
few-shot generalization. In this paper, we provide a first systematic study on
FSCL and present an effective solution with deep neural networks. Our solution
is based on the observation that continual learning of a task sequence
inevitably interferes few-shot generalization, which makes it highly nontrivial
to extend few-shot learning strategies to continual learning scenarios. We draw
inspirations from the robust brain system and develop a method that (1)
interdependently updates a pair of fast / slow weights for continual learning
and few-shot learning to disentangle their divergent objectives, inspired by
the biological model of meta-plasticity and fast / slow synapse; and (2)
applies a brain-inspired two-step consolidation strategy to learn a task
sequence without forgetting in the fast weights while improve generalization
without overfitting in the slow weights. Extensive results on various
benchmarks show that our method achieves a better performance than joint
training of all the tasks ever seen. The ability of few-shot generalization is
also substantially improved from incoming tasks and examples.
