  Farquhar et al. [2021] show that correcting for active learning bias with
underparameterised models leads to improved downstream performance. For
overparameterised models such as NNs, however, correction leads either to
decreased or unchanged performance. They suggest that this is due to an
"overfitting bias" which offsets the active learning bias. We show that depth
uncertainty networks operate in a low overfitting regime, much like
underparameterised models. They should therefore see an increase in performance
with bias correction. Surprisingly, they do not. We propose that this negative
result, as well as the results Farquhar et al. [2021], can be explained via the
lens of the bias-variance decomposition of generalisation error.
