Recent studies have revealed that, beyond conventional accuracy, calibration should also be considered for training modern deep neural networks. To address miscalibration during learning, some methods have explored different penalty functions as part …
For details of this paper, please refer to : Class Adaptive Network Calibration
In spite of the dominant performances of deep neural networks, recent works have shown that they are poorly calibrated, resulting in over-confident predictions. Miscalibration can be exacerbated by overfitting due to the minimization of the …
For details of this paper, please refer to : The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration.