mlsanityData to 7 Oct 2026

Igeood: An Information Geometry Approach to Out-of-Distribution Detection

arXiv 2203.07798 ↗First posted 15 Mar 2022stat.ML cs.LGcited by 20

Not on the map. Why?

Abstract (arXiv)

Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern machine learning (ML) systems. In this paper, we introduce Igeood, an effective method for detecting OOD samples. Igeood applies to any pre-trained neural network, works under various degrees of access to the ML model, does not require OOD samples or assumptions on the OOD data but can also benefit (if available) from OOD samples. By building on the geodesic (Fisher-Rao) distance between the underlying data distributions, our discriminator can combine confidence scores from the logits outputs and the learned features of a deep neural network. Empirically, we show that Igeood outperforms competing state-of-the-art methods on a variety of network architectures and datasets.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 36 of its 66 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

36 of 66 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  7. 2013Describing Textures in the Wildcited by 2,110
  8. 2016Concrete Problems in AI Safetycited by 1,582
  9. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  10. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  11. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  12. 2020Energy-based Out-of-distribution Detectioncited by 1,296
24 more
  1. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  2. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  3. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  4. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  5. 2015Towards Open Set Deep Networkscited by 533
  6. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  7. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  8. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  9. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  10. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 220
  11. 2021A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detectioncited by 206
  12. 2020Contrastive Training for Improved Out-of-Distribution Detectioncited by 186
  13. 2015TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Trackingcited by 165
  14. 2020Understanding the Failure Modes of Out-of-Distribution Generalizationcited by 146
  15. 2020Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encodercited by 145
  16. 2018Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifierscited by 126
  17. 2020Hybrid Models for Open Set Recognitioncited by 109
  18. 2019Out-of-distribution Detection in Classifiers via Generationcited by 49
  19. 2020Multiscale Score Matching for Out-of-Distribution Detectioncited by 36
  20. 2018WAIC, but Why? Generative Ensembles for Robust Anomaly Detectioncited by 25
  21. 2021Removing Undesirable Feature Contributions Using Out-of-Distribution Datacited by 23
  22. 2018Out-of-Distribution Detection using Multiple Semantic Label Representationscited by 22
  23. 2021Adversarial Robustness via Fisher-Rao Regularizationcited by 19
  24. 2020Deep Residual Flow for Out of Distribution Detectioncited by 4

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 8; 2024: 7.

20 papers.

Sort:

By area:

  1. 2022RankFeat: Rank-1 Feature Removal for Out-of-distribution Detectioncited by 52
  2. 2022Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Datacited by 21
  3. 2023Unsupervised Layer-wise Score Aggregation for Textual OOD Detectioncited by 15
  4. 2023Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothnesscited by 15
  5. 2022Hyperbolic VAE via Latent Gaussian Distributionscited by 14
  6. 2024Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Modelscited by 13
  7. 2023Anomaly Detection with Score Distribution Discriminationcited by 7
  8. 2024Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detectioncited by 5
  9. 2022Beyond Mahalanobis-Based Scores for Textual OOD Detectioncited by 3
  10. 2023Toward Stronger Textual Attack Detectorscited by 3
  11. 2023A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detectioncited by 2
  12. 2024Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Controlcited by 2
8 more
  1. 2022The Fisher-Rao Loss for Learning under Label Noisecited by 1
  2. 2023RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detectioncited by 1
  3. 2024Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanismcited by 1
  4. 2024Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detectioncited by 1
  5. 2024Collaborative Knowledge Distillation via a Learning-by-Education Node Communitycited by 1
  6. 2023A Minimax Approach Against Multi-Armed Adversarial Attacks Detectioncited by 0
  7. 2023Distilling the Unknown to Unveil Certaintycited by 0
  8. 2024Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusioncited by 0