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 3001–3010 of 4856 papers

Title	Date	Tasks	Status	Hype
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection	Jan 11, 2022	Anomaly DetectionDictionary Learning	CodeCode Available	0
An Introduction to Autoencoders	Jan 11, 2022	Anomaly DetectionDenoising	—Unverified	0
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models	Jan 10, 2022	Anomaly Detection	CodeCode Available	1
Configurable Independent Component Analysis Preprocessing Accelerator	Jan 10, 2022	Anomaly Detection	—Unverified	0
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model	Jan 9, 2022	Anomaly DetectionAutoML	—Unverified	0
AnomMAN: Detect Anomaly on Multi-view Attributed Networks	Jan 8, 2022	Anomaly Detection	—Unverified	0
Applications of Signature Methods to Market Anomaly Detection	Jan 7, 2022	Anomaly DetectionTime Series	—Unverified	0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection	Jan 7, 2022	Anomaly DetectionOut-of-Distribution Detection	—Unverified	0
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra	Jan 7, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis	Jan 7, 2022	Anomaly Detection	—Unverified	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	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