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 4281–4290 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Learning for Spatio-Temporal Data Mining: A Survey	Jun 11, 2019	Anomaly DetectionDeep Learning	—Unverified	0
Anomaly Detection in High Performance Computers: A Vicinity Perspective	Jun 11, 2019	Anomaly DetectionVocal Bursts Intensity Prediction	—Unverified	0
Time-Series Anomaly Detection Service at Microsoft	Jun 10, 2019	Anomaly DetectionSaliency Detection	CodeCode Available	1
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering	Jun 10, 2019	Anomaly DetectionSparse Learning	CodeCode Available	0
Outlier Exposure with Confidence Control for Out-of-Distribution Detection	Jun 8, 2019	Anomaly DetectionClassification	CodeCode Available	0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS	Jun 7, 2019	Anomaly DetectionIntrusion Detection	—Unverified	0
Deep Semi-Supervised Anomaly Detection	Jun 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available	0
Using anomaly detection to support classification of fast running (packaging) processes	Jun 6, 2019	Anomaly DetectionGeneral Classification	—Unverified	0
An Adaptive Training-less System for Anomaly Detection in Crowd Scenes	Jun 3, 2019	Anomaly Detectionobject-detection	—Unverified	0
Detecting Anomalies in Image Classification by Means of Semantic Relationships	Jun 1, 2019	Anomaly DetectionGeneral Classification	CodeCode Available	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	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