mlsanityData to 7 Oct 2026

Out of Distribution Detection on ImageNet-O

arXiv 2201.09352 ↗First posted 23 Jan 2022cs.CV cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

Out of distribution (OOD) detection is a crucial part of making machine learning systems robust. The ImageNet-O dataset is an important tool in testing the robustness of ImageNet trained deep neural networks that are widely used across a variety of systems and applications. We aim to perform a comparative analysis of OOD detection methods on ImageNet-O, a first of its kind dataset with a label distribution different than that of ImageNet, that has been created to aid research in OOD detection for ImageNet models. As this dataset is fairly new, we aim to provide a comprehensive benchmarking of some of the current state of the art OOD detection methods on this novel dataset. This benchmarking covers a variety of model architectures, settings where we haves prior access to the OOD data versus when we don't, predictive score based approaches, deep generative approaches to OOD detection, and more.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

20 of 36 references.

References · 20

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  3. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  4. 2019Natural Adversarial Examplescited by 1,401
  5. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  6. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  7. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  8. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  9. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  10. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  11. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  12. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 220
8 more
  1. 2021A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detectioncited by 206
  2. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  3. 2019BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modelingcited by 109
  4. 2021Understanding Failures in Out-of-Distribution Detection with Deep Generative Modelscited by 89
  5. 2020Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Featurescited by 64
  6. 2016Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenescited by 50
  7. 2021No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasetscited by 31
  8. 2021An Effective Baseline for Robustness to Distributional Shiftcited by 25

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.

5 papers.

  1. 2022Full-Spectrum Out-of-Distribution Detectioncited by 63
  2. 2024Efficient Vision-Language Pre-training by Cluster Maskingcited by 13
  3. 2023Probabilistic Self-supervised Learning via Scoring Rules Minimizationcited by 4
  4. 2023Gradient-Based Post-Training Quantization: Challenging the Status Quocited by 0
  5. 2025Role-SynthCLIP: A Role Play Driven Diverse Synthetic Data Approachcited by 0