  Despite huge success, deep networks are unable to learn effectively in
sequential multitask learning settings as they forget the past learned tasks
after learning new tasks. Inspired from complementary learning systems theory,
we address this challenge by learning a generative model that couples the
current task to the past learned tasks through a discriminative embedding
space. We learn an abstract level generative distribution in the embedding that
allows the generation of data points to represent the experience. We sample
from this distribution and utilize experience replay to avoid forgetting and
simultaneously accumulate new knowledge to the abstract distribution in order
to couple the current task with past experience. We demonstrate theoretically
and empirically that our framework learns a distribution in the embedding that
is shared across all task and as a result tackles catastrophic forgetting.
