  Continual/lifelong learning from a non-stationary input data stream is a
cornerstone of intelligence. Despite their phenomenal performance in a wide
variety of applications, deep neural networks are prone to forgetting their
previously learned information upon learning new ones. This phenomenon is
called "catastrophic forgetting" and is deeply rooted in the
stability-plasticity dilemma. Overcoming catastrophic forgetting in deep neural
networks has become an active field of research in recent years. In particular,
gradient projection-based methods have recently shown exceptional performance
at overcoming catastrophic forgetting. This paper proposes two
biologically-inspired mechanisms based on sparsity and heterogeneous dropout
that significantly increase a continual learner's performance over a long
sequence of tasks. Our proposed approach builds on the Gradient Projection
Memory (GPM) framework. We leverage k-winner activations in each layer of a
neural network to enforce layer-wise sparse activations for each task, together
with a between-task heterogeneous dropout that encourages the network to use
non-overlapping activation patterns between different tasks. In addition, we
introduce two new benchmarks for continual learning under distributional shift,
namely Continual Swiss Roll and ImageNet SuperDog-40. Lastly, we provide an
in-depth analysis of our proposed method and demonstrate a significant
performance boost on various benchmark continual learning problems.
