mlsanityData to 7 Oct 2026

Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes

arXiv 1805.05396 ↗First posted 14 May 2018cs.LG stat.MLcited by 21

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Abstract (arXiv)

We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model. The confidence score is learned by the meta-model observing the base model succeeding/failing at its task. As features to the meta-model, we investigate linear classifier probes inserted between the various layers of the base model. Our experiments demonstrate that this approach outperforms various baselines in a filtering task, i.e., task of rejecting samples with low confidence. Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with and without added noise. We discuss the importance of confidence scoring to bridge the gap between experimental and real-world applications.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2018.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 2; 2024: 2; 2025: 6; 2026: 2.

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  1. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  2. 2022Post-hoc Uncertainty Learning using a Dirichlet Meta-Modelcited by 31
  3. 2021Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AIcited by 22
  4. 2021An evaluation of word-level confidence estimation for end-to-end automatic speech recognitioncited by 18
  5. 2024MetaToken: Detecting Hallucination in Image Descriptions by Meta Classificationcited by 13
  6. 2024Confidence-based Estimators for Predictive Performance in Model Monitoringcited by 8
  7. 2022A Survey on Uncertainty Toolkits for Deep Learningcited by 4
  8. 2021Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervalscited by 3
  9. 2022Performance Prediction Under Dataset Shiftcited by 3
  10. 2021Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embeddingcited by 2
  11. 2025Bi-directional Model Cascading with Proxy Confidencecited by 2
  12. 2025SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysiscited by 2
9 more
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  2. 2025Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learningcited by 1
  3. 2021Uncertainty Surrogates for Deep Learningcited by 0
  4. 2022Uncertainty Quantification for Rule-Based Modelscited by 0
  5. 2023Assessment of Prediction Intervals Using Uncertainty Characteristics Curvescited by 0
  6. 2025Neurosymbolic AI Transfer Learning Improves Network Intrusion Detectioncited by 0
  7. 2025Guided Uncertainty Learning Using a Post-Hoc Evidential Meta-Modelcited by 0
  8. 2026UA-Net: Uncertainty-Aware Network for TRISO Image Semantic Segmentationcited by 0
  9. 2026MetaErr: Towards Predicting Error Patterns in Deep Neural Networkscited by 0