  Much recent work has been devoted to the problem of ensuring that a neural
network's confidence scores match the true probability of being correct, i.e.
the calibration problem. Of note, it was found that training with focal loss
leads to better calibration than cross-entropy while achieving similar level of
accuracy \cite{mukhoti2020}. This success stems from focal loss regularizing
the entropy of the model's prediction (controlled by the parameter $\gamma$),
thereby reining in the model's overconfidence. Further improvement is expected
if $\gamma$ is selected independently for each training sample
(Sample-Dependent Focal Loss (FLSD-53) \cite{mukhoti2020}). However, FLSD-53 is
based on heuristics and does not generalize well. In this paper, we propose a
calibration-aware adaptive focal loss called AdaFocal that utilizes the
calibration properties of focal (and inverse-focal) loss and adaptively
modifies $\gamma_t$ for different groups of samples based on $\gamma_{t-1}$
from the previous step and the knowledge of model's under/over-confidence on
the validation set. We evaluate AdaFocal on various image recognition and one
NLP task, covering a wide variety of network architectures, to confirm the
improvement in calibration while achieving similar levels of accuracy.
Additionally, we show that models trained with AdaFocal achieve a significant
boost in out-of-distribution detection.
