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 2751–2760 of 4856 papers

Title	Date	Tasks	Status
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection	Jan 19, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection	Jan 19, 2023	Anomaly DetectionClustering	—Unverified
Position Regression for Unsupervised Anomaly Detection	Jan 19, 2023	Anomaly DetectionImage Reconstruction	CodeCode Available
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy	Jan 15, 2023	Anomaly DetectionTime Series	—Unverified
Anomalies, Representations, and Self-Supervision	Jan 11, 2023	Anomaly DetectionContrastive Learning	—Unverified
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing	Jan 9, 2023	Anomaly DetectionBenchmarking	—Unverified
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning	Jan 9, 2023	Anomaly DetectionDeep Learning	—Unverified
Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing	Jan 9, 2023	Anomaly Detection	—Unverified
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection	Jan 7, 2023	Active LearningAnomaly Detection	CodeCode Available
Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions	Jan 5, 2023	Anomaly 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