  Deep Neural Networks (DNN) have shown great promise in many classification
applications, yet are widely known to have poorly calibrated predictions when
they are over-parametrized. Improving DNN calibration without comprising on
model accuracy is of extreme importance and interest in safety critical
applications such as in the health-care sector. In this work, we show that
decoupling the training of feature extraction layers and classification layers
in over-parametrized DNN architectures such as Wide Residual Networks (WRN) and
Visual Transformers (ViT) significantly improves model calibration whilst
retaining accuracy, and at a low training cost. In addition, we show that
placing a Gaussian prior on the last hidden layer outputs of a DNN, and
training the model variationally in the classification training stage, even
further improves calibration. We illustrate these methods improve calibration
across ViT and WRN architectures for several image classification benchmark
datasets.
