Improving Model Calibration by Leveraging Prediction Correctness Awareness

The core message of this paper is that by directly optimizing for high confidence on correctly classified samples and low confidence on incorrectly classified samples, a post-hoc calibrator can achieve better calibration performance compared to commonly used loss functions like cross-entropy and mean squared error.