Understand
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.
Reading the bibliography…