Multi-class Anomaly Detection
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Benchmarks
Most implemented
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
Papers
XMatchAD: A Cross-Modal Matching Perspective on Reconstruction-based Anomaly Detection
The remarkable success of reconstruction-based methods in Unsupervised Anomaly Detection (UAD) lies in their ability to identify and localize anomalies by modeling discrepancies between input images and their reconstruct…
Unsupervised Anomaly DetectionMulti-class Anomaly DetectionBoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
Reconstruction-based anomaly detection is attractive for industrial inspection, but scaling it from category-specific training to a one-for-all setting is challenging. A single model must reconstruct diverse normal appea…
Multi-class Anomaly DetectionUni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection
Multi-modal industrial anomaly detection typically relies on separate models for each product category, fundamentally limiting practical scalability. When shifting to a unified paradigm that handles diverse classes simul…
Multi-class Anomaly DetectionDual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
Multi-class anomaly detection aims to build unified models across diverse product categories. However, as the number of categories grows, its performance often degrades due to increasingly complex and heterogeneous norma…
Unsupervised Anomaly DetectionMulti-class Anomaly DetectionLarge-Scale Universal Defect Generation: Foundation Models and Datasets
Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scale paired defect editing data. This issue is aggravated by substantial …
Multi-class Anomaly DetectionFew-Shot LearningAnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI
Reliable anomaly detection in brain MRI remains challenging due to the scarcity of annotated abnormal cases and the frequent absence of key imaging modalities in real clinical workflows. Existing single-class or multi-cl…
Multi-class Anomaly Detection