mlsanityData to 7 Oct 2026

Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification

arXiv 2101.02447 ↗First posted 7 Jan 2021cs.CV cs.AIcited by 10

Not on the map. Why?

Abstract (arXiv)

We reconsider the evaluation of OOD detection methods for image recognition. Although many studies have been conducted so far to build better OOD detection methods, most of them follow Hendrycks and Gimpel's work for the method of experimental evaluation. While the unified evaluation method is necessary for a fair comparison, there is a question of if its choice of tasks and datasets reflect real-world applications and if the evaluation results can generalize to other OOD detection application scenarios. In this paper, we experimentally evaluate the performance of representative OOD detection methods for three scenarios, i.e., irrelevant input detection, novel class detection, and domain shift detection, on various datasets and classification tasks. The results show that differences in scenarios and datasets alter the relative performance among the methods. Our results can also be used as a guide for practitioners for the selection of OOD detection methods.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 7 Jan 2021.

Builds on

22 of 41 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  5. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  7. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  8. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  9. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  10. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  11. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  12. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
10 more
  1. 2019Confidence Regularized Self-Trainingcited by 418
  2. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  3. 2018Rethinking ImageNet Pre-trainingcited by 376
  4. 2019Using Pre-Training Can Improve Model Robustness and Uncertaintycited by 373
  5. 2018Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifierscited by 126
  6. 2019Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancycited by 112
  7. 2017Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenescited by 105
  8. 2020Unsupervised Domain Adaptation in Semantic Segmentation: a Reviewcited by 88
  9. 2017A Public Image Database for Benchmark of Plant Seedling Classification Algorithmscited by 20
  10. 2019Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine Similaritycited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 0; 2023: 2; 2024: 0; 2025: 2; 2026: 3.

10 papers.

  1. 2021Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environmentscited by 65
  2. 2021Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluationcited by 15
  3. 2023Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regressioncited by 7
  4. 2023Finding Competence Regions in Domain Generalizationcited by 3
  5. 2021Natural Attribute-based Shift Detectioncited by 2
  6. 2026ICONIC-444: A 3.1-Million-Image Dataset for OOD Detection Researchcited by 1
  7. 2025BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networkscited by 0
  8. 2025Benchmarking Out-of-Distribution Detection for Plankton Recognition: A Systematic Evaluation of Advanced Methods in Marine Ecological Monitoringcited by 0
  9. 2026Robust Uncertainty Estimation under Distribution Shift via Difference Reconstructioncited by 0
  10. 2026CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Modelcited by 0