Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection
Fuente:
arXiv
Saved in:
| Main Authors: | Zhou, Ziqing, Pan, Yurui, Wang, Lidong, Zhu, Wenbing, Chi, Mingmin, Wu, Dong, Peng, Bo |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
by: Pan, Yurui, et al.
Published: (2025)
by: Pan, Yurui, et al.
Published: (2025)
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
by: He, Liren, et al.
Published: (2024)
by: He, Liren, et al.
Published: (2024)
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
by: Zhu, Wenbing, et al.
Published: (2025)
by: Zhu, Wenbing, et al.
Published: (2025)
Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection
by: Zhu, Wenbing, et al.
Published: (2026)
by: Zhu, Wenbing, et al.
Published: (2026)
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
by: Dong, Zhijin, et al.
Published: (2024)
by: Dong, Zhijin, et al.
Published: (2024)
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
by: Zhu, Wenbing, et al.
Published: (2025)
by: Zhu, Wenbing, et al.
Published: (2025)
Generalist Graph Anomaly Detection via Prototype-Based Distillation
by: Xu, Yiming, et al.
Published: (2026)
by: Xu, Yiming, et al.
Published: (2026)
Once Is Enough: Lightweight DiT-Based Video Virtual Try-On via One-Time Garment Appearance Injection
by: Pan, Yanjie, et al.
Published: (2025)
by: Pan, Yanjie, et al.
Published: (2025)
Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
by: Feng, Yaoxuan, et al.
Published: (2026)
by: Feng, Yaoxuan, et al.
Published: (2026)
ProtoAnomalyNCD: Prototype Learning for Multi-class Novel Anomaly Discovery in Industrial Scenarios
by: Zhao, Botong, et al.
Published: (2025)
by: Zhao, Botong, et al.
Published: (2025)
Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation
by: Huang, Min, et al.
Published: (2024)
by: Huang, Min, et al.
Published: (2024)
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
by: He, Haoyang, et al.
Published: (2024)
by: He, Haoyang, et al.
Published: (2024)
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection
by: Wang, Xiaolei, et al.
Published: (2024)
by: Wang, Xiaolei, et al.
Published: (2024)
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
by: Meissen, Felix, et al.
Published: (2023)
by: Meissen, Felix, et al.
Published: (2023)
Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
by: Lu, Ruiying, et al.
Published: (2025)
by: Lu, Ruiying, et al.
Published: (2025)
Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
by: Wu, Hai, et al.
Published: (2024)
by: Wu, Hai, et al.
Published: (2024)
ProTPS: Prototype-Guided Text Prompt Selection for Continual Learning
by: Mei, Jie, et al.
Published: (2026)
by: Mei, Jie, et al.
Published: (2026)
Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
by: Iqbal, Nasar, et al.
Published: (2025)
by: Iqbal, Nasar, et al.
Published: (2025)
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection
by: Quan, Jiajie, et al.
Published: (2025)
by: Quan, Jiajie, et al.
Published: (2025)
PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis
by: Li, Yanfei, et al.
Published: (2025)
by: Li, Yanfei, et al.
Published: (2025)
ProMotion: Prototypes As Motion Learners
by: Lu, Yawen, et al.
Published: (2024)
by: Lu, Yawen, et al.
Published: (2024)
Federated Prototype Graph Learning
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection
by: Chen, Yitong, et al.
Published: (2024)
by: Chen, Yitong, et al.
Published: (2024)
A Prototype-Based Neural Network for Image Anomaly Detection and Localization
by: Huang, Chao, et al.
Published: (2023)
by: Huang, Chao, et al.
Published: (2023)
Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection
by: Wang, Fuyun, et al.
Published: (2025)
by: Wang, Fuyun, et al.
Published: (2025)
Learning with Adaptive Prototype Manifolds for Out-of-Distribution Detection
by: Peng, Ningkang, et al.
Published: (2026)
by: Peng, Ningkang, et al.
Published: (2026)
FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
by: Tian, Long, et al.
Published: (2025)
by: Tian, Long, et al.
Published: (2025)
DeformTune: A Deformable XAI Music Prototype for Non-Musicians
by: Xu, Ziqing, et al.
Published: (2025)
by: Xu, Ziqing, et al.
Published: (2025)
Prototype Perturbation for Relaxing Alignment Constraints in Backward-Compatible Learning
by: Zhou, Zikun, et al.
Published: (2025)
by: Zhou, Zikun, et al.
Published: (2025)
Learning with Mixture of Prototypes for Out-of-Distribution Detection
by: Lu, Haodong, et al.
Published: (2024)
by: Lu, Haodong, et al.
Published: (2024)
Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery
by: Wang, Zekun, et al.
Published: (2025)
by: Wang, Zekun, et al.
Published: (2025)
Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications
by: Rahmaniar, Wahyu, et al.
Published: (2026)
by: Rahmaniar, Wahyu, et al.
Published: (2026)
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
by: Jin, Ying, et al.
Published: (2024)
by: Jin, Ying, et al.
Published: (2024)
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
by: Song, Andrew H., et al.
Published: (2024)
by: Song, Andrew H., et al.
Published: (2024)
Center-Oriented Prototype Contrastive Clustering
by: Dong, Shihao, et al.
Published: (2025)
by: Dong, Shihao, et al.
Published: (2025)
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data
by: Im, Jiin, et al.
Published: (2024)
by: Im, Jiin, et al.
Published: (2024)
Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection
by: Jiang, Yuxin, et al.
Published: (2025)
by: Jiang, Yuxin, et al.
Published: (2025)
MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection
by: Beizaee, Farzad, et al.
Published: (2025)
by: Beizaee, Farzad, et al.
Published: (2025)
Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation
by: Ou, Yangxun, et al.
Published: (2024)
by: Ou, Yangxun, et al.
Published: (2024)
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
by: Wang, Fuyun, et al.
Published: (2026)
by: Wang, Fuyun, et al.
Published: (2026)
Similar Items
-
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
by: Pan, Yurui, et al.
Published: (2025) -
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
by: He, Liren, et al.
Published: (2024) -
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
by: Zhu, Wenbing, et al.
Published: (2025) -
Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection
by: Zhu, Wenbing, et al.
Published: (2026) -
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
by: Dong, Zhijin, et al.
Published: (2024)