Titus J. Brinker

Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions

Communicating the predictive uncertainty of deep neural networks transparently and reliably is important in many safety-critical applications such as medicine. However, modern neural networks tend to be poorly calibrated, resulting in wrong predictions made with a high confidence. While existing pos…