  Learning continually is a key aspect of intelligence and a necessary ability
to solve many real-life problems. One of the most effective strategies to
control catastrophic forgetting, the Achilles' heel of continual learning, is
storing part of the old data and replaying them interleaved with new
experiences (also known as the replay approach). Generative replay, which is
using generative models to provide replay patterns on demand, is particularly
intriguing, however, it was shown to be effective mainly under simplified
assumptions, such as simple scenarios and low-dimensional data. In this paper,
we show that, while the generated data are usually not able to improve the
classification accuracy for the old classes, they can be effective as negative
examples (or antagonists) to better learn the new classes, especially when the
learning experiences are small and contain examples of just one or few classes.
The proposed approach is validated on complex class-incremental and
data-incremental continual learning scenarios (CORe50 and ImageNet-1000)
composed of high-dimensional data and a large number of training experiences: a
setup where existing generative replay approaches usually fail.
