Complementary Learning for Overcoming Catastrophic Forgetting Using
Experience Replay
Abstract
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
generative distribution in the embedding that allows
generation of data points to represent past 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, which is shared across all tasks,
and as a result tackles catastrophic forgetting