A decoupled Bayesian neural network that maps a DNN's logits to calibrated probabilities consistently reduces expected calibration error, at the cost of some accuracy loss on complex datasets.
Huang, et al., Densely connected convolutional networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017) 2261–2269
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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
A decoupled Bayesian neural network that maps a DNN's logits to calibrated probabilities consistently reduces expected calibration error, at the cost of some accuracy loss on complex datasets.