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garrus

Garrus. Python framework for better confidence estimate of deep neural networks.

  • 0.2.4
  • PyPI
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In the middle of some calibrations...

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python python python

Garrus is a python framework for better confidence estimate of deep neural networks. Modern networks are overconfident estimators, that makes themselves unreliable and therefore limits the deployment of them in safety-critical applications.

Garrus provides tools for high quality confidence estimation such as confidence calibration and ordinal ranking methods, helping networks to know correctly what they do not know.


Installation:

pip install -U garrus

Documentation:

Roadmap:

  • Core:
    • Calibration metrics:
      • ECE
      • NLL
      • Brier
    • Ordinal Ranking Metrics:
      • AURC
      • E-AURC
      • AUPRE
      • FPR-n%-TPR
    • Visualizations:
      • Reliability Diagram
      • Confidence Histogram
    • Garrus Profiling
  • Confidence Calibration:
    • Scaling:
      • Platt
      • Temperature
    • Binning:
      • Histogram
      • Isotonic Regression
  • Confidence Regularization:
    • Losses:
      • Correctness Ranking Loss
      • Focal Entropy Penalized Loss
    • Language Model Beam Search
  • Confidence Networks:
    • ConfidNet
    • GarrusNet

Citation:

Please use this bibtex if you want to cite this repository in your publications:

@misc{garrus,
    author = {Kalashnikov, Alexander},
    title = {Deep neural networks calibration framework},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/sleep3r/garrus}},
}

References:

Papers
[1] Guo, Chuan, et al. "On calibration of modern neural networks." International Conference on Machine Learning. PMLR, 2017. APA
[2] Moon, Jooyoung, et al. "Confidence-aware learning for deep neural networks." international conference on machine learning. PMLR, 2020.
[3] Kumar, Ananya, Percy Liang, and Tengyu Ma. "Verified uncertainty calibration." arXiv preprint arXiv:1909.10155 (2019).

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