Despite impressive accuracy, deep neural networks are often miscalibrated and
tend to overly confident predictions. Recent techniques like temperature
scaling (TS) and label smoothing (LS) show effectiveness in obtaining a
well-calibrated model by smoothing logits and hard labels with scalar factors,
respectively. However, the use of uniform TS or LS factor may not be optimal
for calibrating models trained on a long-tailed dataset where the model
produces overly confident probabilities for high-frequency classes. In this
study, we propose class-distribution-aware TS (CDA-TS) and LS (CDA-LS) by
incorporating class frequency information in model calibration in the context
of long-tailed distribution. In CDA-TS, the scalar temperature value is
replaced with the CDA temperature vector encoded with class frequency to
compensate for the over-confidence. Similarly, CDA-LS uses a vector smoothing
factor and flattens the hard labels according to their corresponding class
distribution. We also integrate CDA optimal temperature vector with
distillation loss, which reduces miscalibration in self-distillation (SD). We
empirically show that class-distribution-aware TS and LS can accommodate the
imbalanced data distribution yielding superior performance in both calibration
error and predictive accuracy. We also observe that SD with an extremely
imbalanced dataset is less effective in terms of calibration performance. Code
is available in https://github.com/mobarakol/Class-Distribution-Aware-TS-LS.