  Continual Learning has inspired a plethora of approaches and evaluation
settings; however, the majority of them overlooks the properties of a practical
scenario, where the data stream cannot be shaped as a sequence of tasks and
offline training is not viable. We work towards General Continual Learning
(GCL), where task boundaries blur and the domain and class distributions shift
either gradually or suddenly. We address it through mixing rehearsal with
knowledge distillation and regularization; our simple baseline, Dark Experience
Replay, matches the network's logits sampled throughout the optimization
trajectory, thus promoting consistency with its past. By conducting an
extensive analysis on both standard benchmarks and a novel GCL evaluation
setting (MNIST-360), we show that such a seemingly simple baseline outperforms
consolidated approaches and leverages limited resources. We further explore the
generalization capabilities of our objective, showing its regularization being
beneficial beyond mere performance.
