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 3531–3540 of 4856 papers

Title	Date	Tasks	Status
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays	Feb 19, 2021	Anomaly DetectionSelf-Supervised Anomaly Detection	CodeCode Available
Interpretable Stability Bounds for Spectral Graph Filters	Feb 18, 2021	Anomaly DetectionDenoising	—Unverified
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks	Feb 18, 2021	Anomaly DetectionClustering	—Unverified
Topological Obstructions to Autoencoding	Feb 16, 2021	Anomaly DetectionInductive Bias	—Unverified
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data	Feb 15, 2021	Anomaly DetectionGeneral Classification	—Unverified
Towards AIOps in Edge Computing Environments	Feb 12, 2021	Anomaly DetectionCloud Computing	—Unverified
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?	Feb 12, 2021	Anomaly DetectionFraud Detection	—Unverified
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks	Feb 11, 2021	Anomaly DetectionFraud Detection	CodeCode Available
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems	Feb 11, 2021	Anomaly DetectionTime Series Analysis	—Unverified
Mutually exciting point process graphs for modelling dynamic networks	Feb 11, 2021	Anomaly DetectionPoint Processes	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