  Modern deep neural networks can produce badly calibrated predictions,
especially when train and test distributions are mismatched. Training an
ensemble of models and averaging their predictions can help alleviate these
issues. We propose a simple technique to improve calibration, using a different
data augmentation for each ensemble member. We additionally use the idea of
`mixing' un-augmented and augmented inputs to improve calibration when test and
training distributions are the same. These simple techniques improve
calibration and accuracy over strong baselines on the CIFAR10 and CIFAR100
benchmarks, and out-of-domain data from their corrupted versions.
