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 4371–4380 of 4856 papers

Title	Date	Tasks	Status	Hype
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data	Feb 17, 2019	Anomaly DetectionClustering	CodeCode Available	0
KINN: Incorporating Expert Knowledge in Neural Networks	Feb 15, 2019	Anomaly DetectionManagement	—Unverified	0
Street Scene: A new dataset and evaluation protocol for video anomaly detection	Feb 15, 2019	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection	Feb 14, 2019	Anomaly DetectionGeneral Classification	CodeCode Available	0
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks	Feb 12, 2019	Anomaly Detection	—Unverified	0
BINet: Multi-perspective Business Process Anomaly Classification	Feb 8, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available	0
Bounded Fuzzy Possibilistic Method	Feb 8, 2019	Anomaly DetectionClustering	—Unverified	0
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling	Feb 6, 2019	Anomaly DetectionAttribute	CodeCode Available	1
Dictionary learning approach to monitoring of wind turbine drivetrain bearings	Feb 4, 2019	Anomaly DetectionDictionary Learning	—Unverified	0
Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning	Feb 1, 2019	Anomaly DetectionClustering	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