Methods › General › Confidence Calibration › CCAC
Confidence Calibration with an Auxiliary Class)
CCAC
Introduced by Zhihui Shao et al. in Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Confidence Calibration with an Auxiliary Class, or CCAC, is a post-hoc confidence calibration method for DNN classifiers on OOD datasets. The key feature of CCAC is an auxiliary class in the calibration model which separates mis-classified samples from correctly classified ones, thus effectively mitigating the target DNN’s being confidently wrong. It also reduces the number of free parameters in CCAC to reduce free parameters and facilitate transfer to a new unseen dataset.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Model-Based Actor-Critic with Chance Constraint for Stochastic System 19 Dec 2020 · 0 repositories · arXiv:2012.10716
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Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets 16 Jun 2020 · 0 repositories · arXiv:2006.08914
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Model Predictive Control | 1 |
| Reinforcement Learning (RL) | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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