• Corpus ID: 13046179

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

@article{Hendrycks2017ABF,
  title={A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks},
  author={Dan Hendrycks and Kevin Gimpel},
  journal={ArXiv},
  year={2017},
  volume={abs/1610.02136}
}
We consider the two related problems of detecting if an example is misclassified or out-of-distribution. [] Key Result We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks.
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OUT-OF-DISTRIBUTION DETECTION USING DEEP NEURAL NETWORKS
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TLDR
This work proposes a methodology for training a neural network that allows it to efficiently detect outof-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes.
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The proposed ODIN method, based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection, consistently outperforms the baseline approach by a large margin.
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A novel algorithm to generate out-of-distribution samples using a manifold learning network and then train an n+1 classifier for OOD detection, where the $n+1^{th}$ class represents the OOD samples is proposed.
Contrastive Training for Improved Out-of-Distribution Detection
TLDR
This paper proposes and investigates the use of contrastive training to boost OOD detection performance, and introduces and employs the Confusion Log Probability (CLP) score, which quantifies the difficulty of the Ood detection task by capturing the similarity of inlier and outlier datasets.
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
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By training a simple 3-layers neural network on top of the logit activations of an already pretrained neural network, this work shows that this network is able to detect misclassifications at a competitive level.
Class-wise Thresholding for Detecting Out-of-Distribution Data
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The problem of detecting Out-ofDistribution input data when using deep neural networks is considered, and a class-wise thresholding scheme is proposed that can apply to most existing OoD detection algorithms and can maintain similar OoD Detection performance even in the presence of label shift.
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