Gradient Episodic Memory for Continuum Learning

One major obstacle towards artificial intelligence is the poor ability of models to quickly solve new problems, without forgetting previously acquired knowledge. To better understand this issue, we study the problem of learning over a continuum of data, where the model observes, once and one by one, examples concerning an ordered 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 to learn over continuums of data, called Gradient of Episodic Memory (GEM), which 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.