  Miscalibration - a mismatch between a model's confidence and its correctness
- of Deep Neural Networks (DNNs) makes their predictions hard to rely on.
Ideally, we want networks to be accurate, calibrated and confident. We show
that, as opposed to the standard cross-entropy loss, focal loss [Lin et. al.,
2017] allows us to learn models that are already very well calibrated. When
combined with temperature scaling, whilst preserving accuracy, it yields
state-of-the-art calibrated models. We provide a thorough analysis of the
factors causing miscalibration, and use the insights we glean from this to
justify the empirically excellent performance of focal loss. To facilitate the
use of focal loss in practice, we also provide a principled approach to
automatically select the hyperparameter involved in the loss function. We
perform extensive experiments on a variety of computer vision and NLP datasets,
and with a wide variety of network architectures, and show that our approach
achieves state-of-the-art calibration without compromising on accuracy in
almost all cases. Code is available at
https://github.com/torrvision/focal_calibration.
