hendrycks / Error Detection
Licence: mit
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Implementations of the experiments found in A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks by Dan Hendrycks and Kevin Gimpel. https://arxiv.org/abs/1610.02136
Most results are in Jupyter notebooks since several data sets used have licensing restrictions (e.g., TIMIT, WSJ PTB, etc.).
Citation
@article{hendrycks17baseline,
author = {Dan Hendrycks and Kevin Gimpel},
title = {A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks},
journal = {Proceedings of International Conference on Learning Representations},
year = {2017},
}
Follow-up Project
This more recent repository has PyTorch code for a general anomaly detection method which builds on this baseline.
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