# MAEDAY: MAE for few and zero shot AnomalY-Detection

arXiv: https://arxiv.org/abs/2211.14307 · HTML: https://mlsanity.com/paper/2211.14307/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [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)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2207.14315/) (2022)
- [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)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](https://mlsanity.com/paper/2008.12577/) (2020)
- [Registration based Few-Shot Anomaly Detection](https://mlsanity.com/paper/2207.07361/) (2022)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection](https://mlsanity.com/paper/2104.14535/) (2021)
- [FOD-A: A Dataset for Foreign Object Debris in Airports](https://mlsanity.com/paper/2110.03072/) (2021)
- [MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification](https://mlsanity.com/paper/1912.00412/) (2019)
- [Foreign Object Debris Detection for Airport Pavement Images based on Self-supervised Localization and Vision Transformer](https://mlsanity.com/paper/2210.16901/) (2022)

## Built on by (most cited first, 30 of 30)

- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts](https://mlsanity.com/paper/2403.06495/) (2024)
- [Segment Any Anomaly without Training via Hybrid Prompt Regularization](https://mlsanity.com/paper/2305.10724/) (2023)
- [Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2502.07601/) (2025)
- [Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey](https://mlsanity.com/paper/2407.21794/) (2024)
- [Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization](https://mlsanity.com/paper/2308.15939/) (2023)
- [DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup](https://mlsanity.com/paper/2508.13560/) (2025)
- [FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks](https://mlsanity.com/paper/2301.06957/) (2023)
- [What makes a good data augmentation for few-shot unsupervised image anomaly detection?](https://mlsanity.com/paper/2304.03294/) (2023)
- [PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation](https://mlsanity.com/paper/2401.11316/) (2024)
- [Towards Zero-shot 3D Anomaly Localization](https://mlsanity.com/paper/2412.04304/) (2024)
- [High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis](https://mlsanity.com/paper/2304.06433/) (2023)
- [Dual-path Frequency Discriminators for Few-shot Anomaly Detection](https://mlsanity.com/paper/2403.04151/) (2024)
- [Towards Universal Unsupervised Anomaly Detection in Medical Imaging](https://mlsanity.com/paper/2401.10637/) (2024)
- [AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation](https://mlsanity.com/paper/2512.13671/) (2025)
- [Aerial Image Object Detection With Vision Transformer Detector (ViTDet)](https://mlsanity.com/paper/2301.12058/) (2023)
- [UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving](https://mlsanity.com/paper/2406.06370/) (2024)
- [Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling](https://mlsanity.com/paper/2404.08931/) (2024)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [Zero-Shot Anomaly Detection via Batch Normalization](https://mlsanity.com/paper/2302.07849/) (2023)
- [Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion](https://mlsanity.com/paper/2601.08022/) (2026)
- [LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space](https://mlsanity.com/paper/2410.10234/) (2024)
- [Position: Untrained Machine Learning for Anomaly Detection by using 3D Point Cloud Data](https://mlsanity.com/paper/2502.03876/) (2025)
- [Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection](https://mlsanity.com/paper/2510.15602/) (2025)
- [HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2602.09524/) (2026)
- [InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation](https://mlsanity.com/paper/2604.04632/) (2026)
- [Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detection](https://mlsanity.com/paper/2606.01962/) (2026)
- [Identifying the Unknown: Prompt-Free Open Vocabulary Anomaly Recognition for Robot-Object Interaction](https://mlsanity.com/paper/2606.26829/) (2026)
- [GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models](https://mlsanity.com/paper/2607.01049/) (2026)
- [PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images](https://mlsanity.com/paper/2608.04210/) (2026)

## Abstract (arXiv)

We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to reconstruct compared with normal regions. MAEDAY is the first image-reconstruction-based anomaly detection method that utilizes a pre-trained model, enabling its use for Few-Shot Anomaly Detection (FSAD). We also show the same method works surprisingly well for the novel tasks of Zero-Shot AD (ZSAD) and Zero-Shot Foreign Object Detection (ZSFOD), where no normal samples are available. Code is available at https://github.com/EliSchwartz/MAEDAY .