An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

Understand

Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result.

• In recent years, a numerous number of uncertainty quantification methods have been introduced.
• Monte Carlo dropout (MC-Dropout) is one of the most well-known techniques to quantify uncertainty in deep learning methods.
• In this study, we propose two new loss functions by combining cross entropy with Expected Calibration Error (ECE) and Predictive Entropy (PE).