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On Calibration of Modern Neural Networks (2017)

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Terry Taewoong Um
Terry Taewoong Um

The document discusses calibration of modern neural networks. It notes that the softmax output of a network does not necessarily represent confidence. It examines ways to measure miscalibration in networks, including factors that can cause miscalibration like overfitting. Methods to calibrate networks are presented, such as temperature scaling. The findings of comparing these calibration techniques to related works are summarized.

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Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry Taewoong Um ON CALIBRATION OF MODERN NE URAL NETWORKS 1
TODAY’S PAPER Terry Taewoong Um (terry.t.um@gmail.com) https://vimeo.com/238242536
GOAL Terry Taewoong Um (terry.t.um@gmail.com) Softmax output (ෝ𝒑) != Confidence (ෝ𝒒)
MEASURING MISCALIBRATION Terry Taewoong Um (terry.t.um@gmail.com)
MEASURING MISCALIBRATION Terry Taewoong Um (terry.t.um@gmail.com)
MEASURING MISCALIBRATION Terry Taewoong Um (terry.t.um@gmail.com) What factors cause misclalibration?
WHAT CAUSES MISCALIBRATION? Terry Taewoong Um (terry.t.um@gmail.com)
OVERFITTING Terry Taewoong Um (terry.t.um@gmail.com)
CALIBRATION Temperature scaling
COMPARISON TO RELATED WORKS Terry Taewoong Um (terry.t.um@gmail.com) Histogram Binning Plot Scaling Bayesian Binning into Quantiles (BBQ)
FINDINGS & CONCLUSION Terry Taewoong Um (terry.t.um@gmail.com)
MY THOUGHTS Terry Taewoong Um (terry.t.um@gmail.com)
TODAY’S PAPER Terry Taewoong Um (terry.t.um@gmail.com) https://vimeo.com/238242536

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