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 3351–3360 of 4856 papers

Title	Date	Tasks	Status
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model	Jan 9, 2022	Anomaly DetectionAutoML	—Unverified
TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters	Nov 22, 2024	Anomaly DetectionDiversity	—Unverified
TRACE: Time SeRies PArameter EffiCient FinE-tuning	Mar 21, 2025	Anomaly Detectionparameter-efficient fine-tuning	—Unverified
Track Any Anomalous Object:A Granular Video Anomaly Detection Pipeline	Jan 1, 2025	Anomaly DetectionAnomaly Localization	—Unverified
Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline	Jun 5, 2025	Anomaly DetectionAnomaly Localization	—Unverified
Tracking Real-time Anomalies in Cyber-Physical Systems Through Dynamic Behavioral Analysis	Jun 18, 2024	Anomaly DetectionTAG	—Unverified
Tractometry-based Anomaly Detection for Single-subject White Matter Analysis	May 22, 2020	Anomaly DetectionDiffusion MRI	—Unverified
Traffic congestion anomaly detection and prediction using deep learning	Jun 23, 2020	Anomaly DetectionDeep Learning	—Unverified
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection	Feb 2, 2022	Anomaly DetectionIntrusion Detection	—Unverified
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds	Jun 23, 2017	Anomaly DetectionEvent Detection	—Unverified

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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