  Deep Neural Networks ( DNN s) are known to make overconfident mistakes, which
makes their use problematic in safety-critical applications. State-of-the-art (
SOTA ) calibration techniques improve on the confidence of predicted labels
alone and leave the confidence of non-max classes (e.g. top-2, top-5)
uncalibrated. Such calibration is not suitable for label refinement using
post-processing. Further, most SOTA techniques learn a few hyper-parameters
post-hoc, leaving out the scope for image, or pixel specific calibration. This
makes them unsuitable for calibration under domain shift, or for dense
prediction tasks like semantic segmentation. In this paper, we argue for
intervening at the train time itself, so as to directly produce calibrated DNN
models. We propose a novel auxiliary loss function: Multi-class Difference in
Confidence and Accuracy ( MDCA ), to achieve the same MDCA can be used in
conjunction with other application/task-specific loss functions. We show that
training with MDCA leads to better-calibrated models in terms of Expected
Calibration Error ( ECE ), and Static Calibration Error ( SCE ) on image
classification, and segmentation tasks. We report ECE ( SCE ) score of 0.72
(1.60) on the CIFAR 100 dataset, in comparison to 1.90 (1.71) by the SOTA.
Under domain shift, a ResNet-18 model trained on PACS dataset using MDCA gives
an average ECE ( SCE ) score of 19.7 (9.7) across all domains, compared to 24.2
(11.8) by the SOTA. For the segmentation task, we report a 2X reduction in
calibration error on PASCAL - VOC dataset in comparison to Focal Loss. Finally,
MDCA training improves calibration even on imbalanced data, and for natural
language classification tasks. We have released the code here: code is
available at https://github.com/mdca-loss
