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 871–880 of 4856 papers

Title	Date	Tasks	Status	Hype
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series	Apr 23, 2020	Anomaly DetectionRespiratory Failure	CodeCode Available	1
Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging	Apr 17, 2020	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm	Apr 16, 2020	Anomaly DetectionNovelty Detection	CodeCode Available	1
Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model	Apr 9, 2020	Anomaly DetectionTime Series	CodeCode Available	1
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study	Apr 7, 2020	AnatomyAnomaly Detection	CodeCode Available	1
When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos	Apr 6, 2020	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Learning Memory-guided Normality for Anomaly Detection	Mar 30, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection	Mar 27, 2020	Anomaly DetectionBinary Classification	CodeCode Available	1
FastDTW is approximate and Generally Slower than the Algorithm it Approximates	Mar 25, 2020	Anomaly DetectionClustering	CodeCode Available	1
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders	Mar 24, 2020	Anomaly DetectionOut of Distribution (OOD) Detection	CodeCode Available	1

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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