mlsanityData to 7 Oct 2026

iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection

arXiv 2201.02331 ↗First posted 7 Jan 2022cs.LGcited by 27

Not on the map. Why?

Abstract (arXiv)

Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence on inputs outside their training distribution. The deployment of DNNs in safety-critical domains requires detection of out-of-distribution (OOD) data so that DNNs can abstain from making predictions on those. A number of methods have been recently developed for OOD detection, but there is still room for improvement. We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection. It relies on a novel base non-conformity measure and a new aggregation method, used in the inductive conformal anomaly detection framework, thereby guaranteeing a bounded false detection rate. We demonstrate the efficacy of iDECODe by experiments on image and audio datasets, obtaining state-of-the-art results. We also show that iDECODe can detect adversarial examples.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 43 of its 74 references that are in the map. First posted on arXiv 7 Jan 2022.

Builds on

43 of 74 references.

References · 43

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  4. 2016Adversarial examples in the physical worldcited by 1,762
  5. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  6. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  7. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  8. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  9. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  10. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  11. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  12. 2016Group Equivariant Convolutional Networkscited by 1,154
31 more
  1. 2016End to End Learning for Self-Driving Carscited by 1,000
  2. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  3. 2017Dynamic Routing Between Capsulescited by 689
  4. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  5. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  6. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  7. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  8. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  9. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
  10. 2018Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionalitycited by 280
  11. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  12. 2017Detecting Adversarial Samples from Artifactscited by 244
  13. 2020Classification-Based Anomaly Detection for General Datacited by 210
  14. 2018To Trust Or Not To Trust A Classifiercited by 205
  15. 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 203
  16. 2021Testing for Outliers with Conformal p-valuescited by 133
  17. 2019The Odds are Odd: A Statistical Test for Detecting Adversarial Examplescited by 75
  18. 2018Triplet-Center Loss for Multi-View 3D Object Retrievalcited by 69
  19. 2019Towards neural networks that provably know when they don't knowcited by 69
  20. 2020Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systemscited by 46
  21. 2019Prediction and outlier detection in classification problemscited by 44
  22. 2020Multiscale Score Matching for Out-of-Distribution Detectioncited by 36
  23. 2018Open Category Detection with PAC Guaranteescited by 35
  24. 2019Learning Sound Event Classifiers from Web Audio with Noisy Labelscited by 22
  25. 2019Entropic Out-of-Distribution Detectioncited by 21
  26. 2019AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformationscited by 15
  27. 2021Learning Augmentation Distributions using Transformed Risk Minimizationcited by 13
  28. 2020Adversarial Examples Detection and Analysis with Layer-wise Autoencoderscited by 9
  29. 2021Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODscited by 9
  30. 2021Detecting OODs as datapoints with High Uncertaintycited by 7
  31. 2020Learning with Out-of-Distribution Data for Audio Classificationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 7; 2024: 5; 2025: 7; 2026: 1.

27 papers.

Sort:

By area:

  1. 2022Conformal Prediction Under Feedback Covariate Shift for Biomolecular Designcited by 53
  2. 2023Conformal Prediction Regions for Time Series using Linear Complementarity Programmingcited by 33
  3. 2024Addressing Uncertainty in LLMs to Enhance Reliability in Generative AIcited by 15
  4. 2023Memory-Consistent Neural Networks for Imitation Learningcited by 14
  5. 2022Targeted Adversarial Attacks against Neural Network Trajectory Predictorscited by 11
  6. 2025Conformal Inference under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularizationcited by 11
  7. 2022PAC-Wrap: Semi-Supervised PAC Anomaly Detectioncited by 10
  8. 2022In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?cited by 9
  9. 2025Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guaranteescited by 9
  10. 2024Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detectioncited by 8
  11. 2022Boosting Out-of-Distribution Detection with Multiple Pre-trained Modelscited by 7
  12. 2024Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)cited by 7
15 more
  1. 2024Taming False Positives in Out-of-Distribution Detection with Human Feedbackcited by 7
  2. 2025Conformal Information Pursuit for Interactively Guiding Large Language Modelscited by 7
  3. 2023Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solvingcited by 6
  4. 2024Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Surveycited by 6
  5. 2023Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detectioncited by 5
  6. 2023Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape Templatescited by 4
  7. 2025Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarioscited by 3
  8. 2026From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agentscited by 3
  9. 2022Multiple Testing Framework for Out-of-Distribution Detectioncited by 2
  10. 2022CODiT: Conformal Out-of-Distribution Detection in Time-Series Datacited by 2
  11. 2025Removing Geometric Bias in One-Class Anomaly Detection with Adaptive Feature Perturbationcited by 2
  12. 2023Detection of Adversarial Physical Attacks in Time-Series Image Datacited by 1
  13. 2023Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissionscited by 1
  14. 2025EquiReg: Equivariance Regularized Diffusion for Inverse Problemscited by 1
  15. 2025Monitor and Recover: A Paradigm for Future Research on Distribution Shift in Learning-Enabled Cyber-Physical Systemscited by 0