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 1721–1730 of 4856 papers

Title	Date	Tasks	Status
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems	Oct 10, 2023	Anomaly DetectionFault Detection	—Unverified
Self-Discriminative Modeling for Anomalous Graph Detection	Oct 10, 2023	Anomaly Detection	—Unverified
A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults	Oct 10, 2023	Anomaly DetectionClustering	—Unverified
Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection	Oct 10, 2023	Anomaly Detection	CodeCode Available
AnoDODE: Anomaly Detection with Diffusion ODE	Oct 10, 2023	Anomaly DetectionAnomaly Localization	—Unverified
Enhancing Interpretability and Generalizability in Extended Isolation Forests	Oct 9, 2023	Anomaly DetectionFeature Importance	CodeCode Available
Knowledge Distillation for Anomaly Detection	Oct 9, 2023	Anomaly DetectionKnowledge Distillation	—Unverified
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection	Oct 9, 2023	Anomaly DetectionTime Series	—Unverified
A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection	Oct 9, 2023	Anomaly DetectionEdge-computing	—Unverified
Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection	Oct 8, 2023	Anomaly DetectionTime Series	—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	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