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 1751–1760 of 4856 papers

Title	Date	Tasks	Status	Hype
MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection	Sep 30, 2023	Anomaly DetectionDefect Detection	—Unverified	0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks	Sep 30, 2023	Anomaly DetectionData Augmentation	—Unverified	0
Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection	Sep 30, 2023	Anomaly DetectionTime Series	CodeCode Available	1
Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks	Sep 29, 2023	Anomaly DetectionTemporal Sequences	—Unverified	0
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series	Sep 28, 2023	Anomaly DetectionTime Series	—Unverified	0
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention	Sep 28, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	0
ADGym: Design Choices for Deep Anomaly Detection	Sep 27, 2023	Anomaly DetectionCloud Computing	CodeCode Available	1
Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems	Sep 27, 2023	Anomaly Detection	CodeCode Available	0
Human Kinematics-inspired Skeleton-based Video Anomaly Detection	Sep 27, 2023	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets	Sep 26, 2023	Anomaly ClassificationAnomaly Detection	—Unverified	0

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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