  Attempts to train a comprehensive artificial intelligence capable of solving
multiple tasks have been impeded by a chronic problem called catastrophic
forgetting. Although simply replaying all previous data alleviates the problem,
it requires large memory and even worse, often infeasible in real world
applications where the access to past data is limited. Inspired by the
generative nature of hippocampus as a short-term memory system in primate
brain, we propose the Deep Generative Replay, a novel framework with a
cooperative dual model architecture consisting of a deep generative model
("generator") and a task solving model ("solver"). With only these two models,
training data for previous tasks can easily be sampled and interleaved with
those for a new task. We test our methods in several sequential learning
settings involving image classification tasks.
