MAEDAY: MAE for few and zero shot AnomalY-Detection

2022/11/25 by Eli Schwartz, Assaf Arbelle, Schwartz, Eli +11 · 8 citations
Computer Science · Medicine · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2211.14307

openalex publication_date 2022/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 .

Cited by

• AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation
• Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection
• DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup
• A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects
• RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection
• LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning
• PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images
• AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection

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