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 2311–2320 of 4856 papers

Title	Date	Tasks	Status	Hype
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection	Jan 19, 2023	Anomaly DetectionClustering	—Unverified	0
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection	Jan 19, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection	Jan 17, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks	Jan 17, 2023	Anomaly Detection	CodeCode Available	1
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy	Jan 15, 2023	Anomaly DetectionTime Series	—Unverified	0
Anomalies, Representations, and Self-Supervision	Jan 11, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing	Jan 9, 2023	Anomaly DetectionBenchmarking	—Unverified	0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning	Jan 9, 2023	Anomaly DetectionDeep Learning	—Unverified	0
On Advantages of Mask-level Recognition for Outlier-aware Segmentation	Jan 9, 2023	Anomaly DetectionSemantic Segmentation	CodeCode Available	1
Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing	Jan 9, 2023	Anomaly 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