# Back to the Basics: Revisiting Out-of-Distribution Detection Baselines

arXiv: https://arxiv.org/abs/2207.03061 · HTML: https://mlsanity.com/paper/2207.03061/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [Do Deep Generative Models Know What They Don't Know?](https://mlsanity.com/paper/1810.09136/) (2018)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)
- [A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection](https://mlsanity.com/paper/2106.09022/) (2021)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [Deep Nearest Neighbor Anomaly Detection](https://mlsanity.com/paper/2002.10445/) (2020)
- [Understanding Failures in Out-of-Distribution Detection with Deep Generative Models](https://mlsanity.com/paper/2107.06908/) (2021)
- [Robust Out-of-distribution Detection for Neural Networks](https://mlsanity.com/paper/2003.09711/) (2020)
- [Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles](https://mlsanity.com/paper/1906.07380/) (2019)
- [Analysis of KNN Density Estimation](https://mlsanity.com/paper/2010.00438/) (2020)

## Built on by (most cited first, 16 of 16)

- [Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness](https://mlsanity.com/paper/2310.02832/) (2023)
- [How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?](https://mlsanity.com/paper/2302.03679/) (2023)
- [Continual Evidential Deep Learning for Out-of-Distribution Detection](https://mlsanity.com/paper/2309.02995/) (2023)
- [FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector](https://mlsanity.com/paper/2412.05293/) (2024)
- [Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise](https://mlsanity.com/paper/2308.06861/) (2023)
- [Data Taggants: Dataset Ownership Verification via Harmless Targeted Data Poisoning](https://mlsanity.com/paper/2410.09101/) (2024)
- [TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection](https://mlsanity.com/paper/2412.05292/) (2024)
- [Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks](https://mlsanity.com/paper/2307.07002/) (2023)
- [WeiPer: OOD Detection using Weight Perturbations of Class Projections](https://mlsanity.com/paper/2405.17164/) (2024)
- [Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification](https://mlsanity.com/paper/2303.16616/) (2023)
- [DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors](https://mlsanity.com/paper/2606.11616/) (2026)
- [Detecting Errors in a Numerical Response via any Regression Model](https://mlsanity.com/paper/2305.16583/) (2023)
- [Image Outlier Detection Without Training using RANSAC](https://mlsanity.com/paper/2307.12301/) (2023)
- [Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere](https://mlsanity.com/paper/2405.16460/) (2024)
- [ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies](https://mlsanity.com/paper/2502.01335/) (2025)
- [A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Models](https://mlsanity.com/paper/2509.24420/) (2025)

## Abstract (arXiv)

We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations. Evaluating the OOD detection performance of various methods when utilized with ResNet-50 and Swin Transformer models, we find methods that solely consider the model's predictions can be easily outperformed by also considering the learned representations. Based on our analysis, we advocate for a dead-simple approach that has been neglected in other studies: simply flag as OOD images whose average distance to their K nearest neighbors is large (in the representation space of an image classifier trained on the in-distribution data).