On Calibration of Modern Neural Networks.

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On Calibration of Modern Neural Networks. Proceedings of the 34th International Conference on Machine Learning, 1321-1330.

Type

Conference paper

Intellectual base

Primary research

Year

2017

Link

https://proceedings.mlr.press/v70/guo17a.html

Cited from

computer_science, statistics_experimental_design

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Calibration
  • In machine learning, recognizing that a model can be accurate without being calibrated opens new strategies, as Guo, Pleiss, Sun, and Weinberger (2017) demonstrated for modern deep neural networks.

    This sourceDemonstrates that modern deep networks are accurate but poorly calibrated and that temperature scaling (a single-parameter Platt variant) restores calibration post-hoc without retraining.

• Confidence Annotation
  • In machine learning it is calibrated confidence scores and ensemble disagreement that feed abstention thresholds and human-in-the-loop routing.

    This sourceShows modern classifiers are miscalibrated and corrects them (temperature scaling) so confidence scores match accuracy; supports calibrated-score and abstention-routing claims.

• Out Of Distribution Detection
  • Calibration is computed on in-distribution cases and is blind to novel ones — a perfectly calibrated model still answers confidently and wrongly out of scope.

    This sourceShows modern networks are systematically miscalibrated and that calibration methods (e.g., temperature scaling) are tuned on in-distribution validation data; supports the point that calibration fixes confidence within the training distribution, not out of scope.

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