Structured dropout improves confidence calibration in CNNs by promoting ensemble diversity, with empirical support on SVHN, CIFAR-10, CIFAR-100 and in Bayesian active learning.
What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems, pages 5574–5584, 2017
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Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
Structured dropout improves confidence calibration in CNNs by promoting ensemble diversity, with empirical support on SVHN, CIFAR-10, CIFAR-100 and in Bayesian active learning.