Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 31–40 of 4856 papers

Title	Date	Tasks	Status	Hype
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection	Mar 4, 2025	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	3
Hawk: Learning to Understand Open-World Video Anomalies	May 27, 2024	Anomaly DetectionQuestion Answering	CodeCode Available	3
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond	Mar 21, 2024	Anomaly DetectionDeep Learning	CodeCode Available	3
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection	May 23, 2024	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	3
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection	Jul 22, 2024	Anomaly DetectionLanguage Modeling	CodeCode Available	3
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images	Mar 19, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	3
GluonTS: Probabilistic Time Series Models in Python	Jun 12, 2019	Anomaly DetectionTime Series	CodeCode Available	3
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series	Feb 16, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	3
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models	Aug 29, 2023	Anomaly DetectionIn-Context Learning	CodeCode Available	3
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP	Mar 9, 2025	Anomaly DetectionAnomaly Localization	CodeCode Available	3

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified