Papers › A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks, Kevin Gimpel
We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks.
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Code
Syntology Ran 7 of 21 code samples harvested from 7 repositories linked to this paper; 14 have no recorded run. Of those that ran: 4 ran · honoured contract; 3 ran · our draft was wrong.
By repository: official repository: 14 samples from 1 repository, 0 ran; community (archive-listed): 7 samples from 6 repositories, 7 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
14 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
21 samples harvested; 7 ran; 4 honoured the contract we drafted; 14 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 6 of the 21 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 7 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Out-of-Distribution Detection | CIFAR-10 vs CIFAR-100 | WRN 40-2 (MSP Baseline) | AUPR | 55.8 | #13 of 14 | Archive leaderboard | report |
| Out-of-Distribution Detection | CIFAR-10 vs CIFAR-100 | WRN 40-2 (MSP Baseline) | AUROC | 87.9 | #13 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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