• 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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1,396 Citations
Highly Influential Citations
348
Background Citations
700
Methods Citations
711
Results Citations
45

1,396 Citations

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This work proposes a method of learning confidence estimates for neural networks that is simple to implement and produces intuitively interpretable outputs, and addresses the problem of calibrating out-of-distribution detectors.
Principled Detection of Out-of-Distribution Examples in Neural Networks
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ODIN is proposed, a simple and effective out-of-distribution detector for neural networks, that does not require any change to a pre-trained model, and is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions of in and out of distribution samples, allowing for more effective detection.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
TLDR
A novel training method for classifiers so that such inference algorithms can work better, and it is demonstrated its effectiveness using deep convolutional neural networks on various popular image datasets.
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
TLDR
It is found that characterizing activity patterns by Gram matrices and identifying anomalies in gram matrix values can yield high OOD detection rates, and this method generally performs better than or equal to state-of-the-art Ood detection methods.
A Simple Test-Time Method for Out-of-Distribution Detection
TLDR
A simple Test-time Linear Training (ETLT) method for OOD detection by simply learning a linear regression model trained from the paired image features and inferred OOD scores at test-time, which achieves promising performance and is more practical in real-world applications.
OUT-OF-DISTRIBUTION DETECTION USING DEEP NEURAL NETWORKS
  • Computer Science
  • 2019
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.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
TLDR
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.
Out-of-distribution Detection in Classifiers via Generation
TLDR
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
TLDR
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.
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