  One major obstacle towards AI is the poor ability of models to solve new
problems quicker, and without forgetting previously acquired knowledge. To
better understand this issue, we study the problem of continual learning, where
the model observes, once and one by one, examples concerning a sequence of
tasks. First, we propose a set of metrics to evaluate models learning over a
continuum of data. These metrics characterize models not only by their test
accuracy, but also in terms of their ability to transfer knowledge across
tasks. Second, we propose a model for continual learning, called Gradient
Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial
transfer of knowledge to previous tasks. Our experiments on variants of the
MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when
compared to the state-of-the-art.
