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 4631–4640 of 4856 papers

Title	Date	Tasks	Status
Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection	Apr 5, 2025	Anomaly DetectionDescriptive	CodeCode Available
xSemAD: Explainable Semantic Anomaly Detection in Event Logs Using Sequence-to-Sequence Models	Jun 28, 2024	Anomaly Detection	CodeCode Available
Exploring Deep Anomaly Detection Methods Based on Capsule Net	Jul 15, 2019	Anomaly DetectionPrediction	CodeCode Available
Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone	Dec 28, 2023	Anomaly Detection	CodeCode Available
CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method	Jul 7, 2023	Anomaly Detection	CodeCode Available
Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing	Jun 10, 2017	Anomaly Detectionparameter estimation	CodeCode Available
Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative	Nov 30, 2022	Anomaly DetectionMedical Image Analysis	CodeCode Available
Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements	May 25, 2024	Anomaly DetectionChange Point Detection	CodeCode Available
Explaining Anomalies in Groups with Characterizing Subspace Rules	Aug 20, 2017	Anomaly DetectionDescriptive	CodeCode Available
Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection	Jun 17, 2025	Anomaly Detection	CodeCode Available

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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