mlsanityData to 14 Oct 2026

How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?

arXiv 2312.14452 ↗First posted 22 Dec 2023cs.LGcited by 15

Not on the map. Why?

Abstract (arXiv)

Machine learning models deployed in the wild can be challenged by out-of-distribution (OOD) data from unknown classes. Recent advances in OOD detection rely on distance measures to distinguish samples that are relatively far away from the in-distribution (ID) data. Despite the promise, distance-based methods can suffer from the curse-of-dimensionality problem, which limits the efficacy in high-dimensional feature space. To combat this problem, we propose a novel framework, Subspace Nearest Neighbor (SNN), for OOD detection. In training, our method regularizes the model and its feature representation by leveraging the most relevant subset of dimensions (i.e. subspace). Subspace learning yields highly distinguishable distance measures between ID and OOD data. We provide comprehensive experiments and ablations to validate the efficacy of SNN. Compared to the current best distance-based method, SNN reduces the average FPR95 by 15.96% on the CIFAR-100 benchmark.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 37 of its 63 references that are in the map. First posted on arXiv 22 Dec 2023.

Builds on

37 of 63 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  4. 2013Describing Textures in the Wildcited by 2,125
  5. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  6. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  7. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
  8. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  9. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  10. 2019When Does Label Smoothing Help?cited by 988
  11. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  12. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
25 more
  1. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  2. 2019Measuring Calibration in Deep Learningcited by 446
  3. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  4. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  5. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  6. 2020Calibrating Deep Neural Networks using Focal Losscited by 357
  7. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
  8. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  9. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  10. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  11. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  12. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  13. 2015TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Trackingcited by 165
  14. 2022POEM: Out-of-Distribution Detection with Posterior Samplingcited by 108
  15. 2022Training OOD Detectors in their Natural Habitatscited by 86
  16. 2021Leveraging Sparse Linear Layers for Debuggable Deep Networkscited by 82
  17. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  18. 2019Towards neural networks that provably know when they don't knowcited by 69
  19. 2023Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detectioncited by 57
  20. 2018Discriminative out-of-distribution detection for semantic segmentationcited by 47
  21. 2022Harnessing Out-Of-Distribution Examples via Augmenting Content and Stylecited by 43
  22. 2023Out-of-distribution Detection with Implicit Outlier Transformationcited by 43
  23. 2019Learning Sparse Networks Using Targeted Dropoutcited by 40
  24. 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sourcescited by 31
  25. 2022A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptionscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 8; 2026: 4.

15 papers.

Sort:

By area:

  1. 2024DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automatoncited by 5
  2. 2024WeiPer: OOD Detection using Weight Perturbations of Class Projectionscited by 4
  3. 2025Out-of-Distribution Detection using Synthetic Data Generationcited by 4
  4. 2025Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statisticscited by 2
  5. 2024Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentationcited by 1
  6. 2025Contrastive Representation Modeling for Anomaly Detectioncited by 1
  7. 2025Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imagingcited by 1
  8. 2025OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detectioncited by 1
  9. 2025Pseudo-label Induced Subspace Representation Learning for Robust Out-of-Distribution Detectioncited by 0
  10. 2025Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decompositioncited by 0
  11. 2025Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectoriescited by 0
  12. 2026DAVIS: OOD Detection via Dominant Activations and Variance for Increased Separationcited by 0
3 more
  1. 2026Catalyst: Out-of-Distribution Detection via Elastic Scalingcited by 0
  2. 2026An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencingcited by 0
  3. 2026AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labelscited by 0