  The catastrophic forgetting of previously learnt classes is one of the main
obstacles to the successful development of a reliable and accurate generative
continual learning model. When learning new classes, the internal
representation of previously learnt ones can often be overwritten, resulting in
the model's "memory" of earlier classes being lost over time. Recent
developments in neuroscience have uncovered a method through which the brain
avoids its own form of memory interference. Applying a targeted exaggeration of
the differences between features of similar, yet competing memories, the brain
can more easily distinguish and recall them. In this paper, the application of
such exaggeration, via the repulsion of replayed samples belonging to competing
classes, is explored. Through the development of a 'reconstruction repulsion'
loss, this paper presents a new state-of-the-art performance on the
classification of early classes in the class-incremental learning dataset
CIFAR100.
