  The staple of human intelligence is the capability of acquiring knowledge in
a continuous fashion. In stark contrast, Deep Networks forget catastrophically
and, for this reason, the sub-field of Class-Incremental Continual Learning
fosters methods that learn a sequence of tasks incrementally, blending
sequentially-gained knowledge into a comprehensive prediction.
  This work aims at assessing and overcoming the pitfalls of our previous
proposal Dark Experience Replay (DER), a simple and effective approach that
combines rehearsal and Knowledge Distillation. Inspired by the way our minds
constantly rewrite past recollections and set expectations for the future, we
endow our model with the abilities to i) revise its replay memory to welcome
novel information regarding past data ii) pave the way for learning yet unseen
classes.
  We show that the application of these strategies leads to remarkable
improvements; indeed, the resulting method - termed eXtended-DER (X-DER) -
outperforms the state of the art on both standard benchmarks (such as CIFAR-100
and miniImagenet) and a novel one here introduced. To gain a better
understanding, we further provide extensive ablation studies that corroborate
and extend the findings of our previous research (e.g. the value of Knowledge
Distillation and flatter minima in continual learning setups).
