  Continual Learning (CL) focuses on maximizing the predictive performance of a
model across a non-stationary stream of data. Unfortunately, CL models tend to
forget previous knowledge, thus often underperforming when compared with an
offline model trained jointly on the entire data stream. Given that any CL
model will eventually make mistakes, it is of crucial importance to build
calibrated CL models: models that can reliably tell their confidence when
making a prediction. Model calibration is an active research topic in machine
learning, yet to be properly investigated in CL. We provide the first empirical
study of the behavior of calibration approaches in CL, showing that CL
strategies do not inherently learn calibrated models. To mitigate this issue,
we design a continual calibration approach that improves the performance of
post-processing calibration methods over a wide range of different benchmarks
and CL strategies. CL does not necessarily need perfect predictive models, but
rather it can benefit from reliable predictive models. We believe our study on
continual calibration represents a first step towards this direction.
