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 4541–4550 of 4856 papers

Title	Date	Tasks	Status
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs	May 18, 2018	Anomaly Detection	—Unverified
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training	May 17, 2018	Anomaly DetectionDecoder	CodeCode Available
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders	May 9, 2018	Anomaly Detection	CodeCode Available
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic	May 9, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Incorporating Privileged Information to Unsupervised Anomaly Detection	May 6, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Population Anomaly Detection through Deep Gaussianization	May 5, 2018	Anomaly Detection	—Unverified
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings	May 3, 2018	Anomaly DetectionChange Detection	—Unverified
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models	May 3, 2018	Anomaly DetectionClustering	—Unverified
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.	May 1, 2018	Action DetectionAnomaly Detection	—Unverified
Dysarthric speech evaluation: automatic and perceptual approaches	May 1, 2018	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