RefCal uses a supervised contrastive loss to promote refinement alongside calibration in DNN training, reporting better accuracy, refinement, and ECE than baselines on imbalanced CIFAR-100-LT.
Beyond temper- ature scaling: Obtaining well-calibrated multiclass probabilities with dirichlet calibration.arXiv preprint arXiv:1910.12656,
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Enhancing Deep Neural Network Reliability with Refinement and Calibration
RefCal uses a supervised contrastive loss to promote refinement alongside calibration in DNN training, reporting better accuracy, refinement, and ECE than baselines on imbalanced CIFAR-100-LT.