  Continual learning aims to learn tasks sequentially, with (often severe)
constraints on the storage of old learning samples, without suffering from
catastrophic forgetting. In this work, we propose prescient continual learning,
a novel experimental setting, to incorporate existing information about the
classes, prior to any training data. Usually, each task in a traditional
continual learning setting evaluates the model on present and past classes, the
latter with a limited number of training samples. Our setting adds future
classes, with no training samples at all. We introduce Ghost Model, a
representation-learning-based model for continual learning using ideas from
zero-shot learning. A generative model of the representation space in concert
with a careful adjustment of the losses allows us to exploit insights from
future classes to constraint the spatial arrangement of the past and current
classes. Quantitative results on the AwA2 and aP\&Y datasets and detailed
visualizations showcase the interest of this new setting and the method we
propose to address it.
