On Calibration and Conformal Prediction of Deep Classifiers
Lahav Dabah Tom Tirer
In many classification applications, the prediction of a deep neural network
(DNN) based classifier needs to be accompanied with some confidence indication.
Two popular post-processing approaches for that aim are: 1) calibration:
modifying the classifier's softmax values such that their maximum (associated
with the prediction) better estimates the correctness probability; and 2)
conformal prediction (CP): devising a score (based on the softmax values) from
which a set of predictions with theoretically guaranteed marginal coverage of
the correct class is produced. While in practice both types of indications can
be desired, so far the interplay between them has not been investigated. Toward
filling this gap, in this paper we study the effect of temperature scaling,
arguably the most common calibration technique, on prominent CP methods. We
start with an extensive empirical study that among other insights shows that,
surprisingly, calibration has a detrimental effect on popular adaptive CP
methods: it frequently leads to larger prediction sets. Then, we turn to
theoretically analyze this behavior. We reveal several mathematical properties
of the procedure, according to which we provide a reasoning for the phenomenon.
Our study suggests that it may be worthwhile to utilize adaptive CP methods,
chosen for their enhanced conditional coverage, based on softmax values prior
to (or after canceling) temperature scaling calibration.