Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
neural information processing systems, 2020.
Predicting calibrated confidence scores for multi-class deep networks is important for avoiding rare but costly mistakes. A common approach is to learn a post-hoc calibration function that transforms the output of the original network into calibrated confidence scores while maintaining the network's accuracy. However, previous post-hoc ...More
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