# A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

arXiv: https://arxiv.org/abs/2204.11161 · HTML: https://mlsanity.com/paper/2204.11161/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 170 references that are in the map.
- First posted on arXiv 24 Apr 2022.

## Builds on (most cited first, 30 of 61)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances](https://mlsanity.com/paper/2007.08176/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2104.10036/) (2021)
- [Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2006.16067/) (2020)
- [Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](https://mlsanity.com/paper/2008.12577/) (2020)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://mlsanity.com/paper/2110.14051/) (2021)
- [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://mlsanity.com/paper/1903.08536/) (2019)
- [Learning and Evaluating Representations for Deep One-class Classification](https://mlsanity.com/paper/2011.02578/) (2020)

## Built on by (most cited first, 21 of 21)

- [Foundation Models and Transformers for Anomaly Detection: A Survey](https://mlsanity.com/paper/2507.15905/) (2025)
- [Failure Identification in Imitation Learning Via Statistical and Semantic Filtering](https://mlsanity.com/paper/2604.13788/) (2026)
- [Building Machine Learning Challenges for Anomaly Detection in Science](https://mlsanity.com/paper/2503.02112/) (2025)
- [AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer](https://mlsanity.com/paper/2511.06687/) (2025)
- [VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion](https://mlsanity.com/paper/2511.08173/) (2025)
- [ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects](https://mlsanity.com/paper/2503.04997/) (2025)
- [Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond](https://mlsanity.com/paper/2402.07281/) (2024)
- [Domain-independent detection of known anomalies](https://mlsanity.com/paper/2407.02910/) (2024)
- [Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection](https://mlsanity.com/paper/2503.23451/) (2025)
- [Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology](https://mlsanity.com/paper/2506.19234/) (2025)
- [AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain](https://mlsanity.com/paper/2509.03179/) (2025)
- [Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection](https://mlsanity.com/paper/2407.09578/) (2024)
- [Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection](https://mlsanity.com/paper/2512.06171/) (2025)
- [Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling](https://mlsanity.com/paper/2602.12976/) (2026)
- [Native Extrapolation Awareness in Flow-Based Conditional Generation](https://mlsanity.com/paper/2602.13061/) (2026)
- [BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2602.13091/) (2026)
- [GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detection](https://mlsanity.com/paper/2603.15300/) (2026)
- [Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection](https://mlsanity.com/paper/2605.29455/) (2026)
- [Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection](https://mlsanity.com/paper/2606.04599/) (2026)
- [Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI](https://mlsanity.com/paper/2608.16725/) (2026)
- [TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization](https://mlsanity.com/paper/2609.39033/) (2026)

## Abstract (arXiv)

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.