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 3491–3500 of 4856 papers

Title	Date	Tasks	Status
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation	Jul 24, 2021	Anomaly DetectionGenerative Adversarial Network	—Unverified
HURRA! Human readable router anomaly detection	Jul 23, 2021	Anomaly Detection	—Unverified
Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments	Jul 23, 2021	Anomaly Detectionobject-detection	—Unverified
Anomaly Detection via Self-organizing Map	Jul 21, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary	Jul 21, 2021	Anomaly DetectionDensity Estimation	—Unverified
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in Images	Jul 20, 2021	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection	Jul 20, 2021	Anomaly DetectionDeep Learning	—Unverified
Image-Hashing-Based Anomaly Detection for Privacy-Preserving Online Proctoring	Jul 20, 2021	Anomaly DetectionPrivacy Preserving	—Unverified
OnlineSTL: Scaling Time Series Decomposition by 100x	Jul 19, 2021	Anomaly DetectionChange Point Detection	—Unverified
Explainable Debugger for Black-box Machine Learning Models	Jul 18, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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