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 3281–3290 of 4856 papers

Title	Date	Tasks	Status
An Introduction to Autoencoders	Jan 11, 2022	Anomaly DetectionDenoising	—Unverified
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection	Jan 11, 2022	Anomaly DetectionDictionary Learning	CodeCode Available
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation	Jan 11, 2022	Active LearningAnomaly Detection	—Unverified
Configurable Independent Component Analysis Preprocessing Accelerator	Jan 10, 2022	Anomaly Detection	—Unverified
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model	Jan 9, 2022	Anomaly DetectionAutoML	—Unverified
AnomMAN: Detect Anomaly on Multi-view Attributed Networks	Jan 8, 2022	Anomaly Detection	—Unverified
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra	Jan 7, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Persistent Homology for Breast Tumor Classification using Mammogram Scans	Jan 7, 2022	Anomaly DetectionClassification	—Unverified
Applications of Signature Methods to Market Anomaly Detection	Jan 7, 2022	Anomaly DetectionTime Series	—Unverified
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection	Jan 7, 2022	Anomaly DetectionOut-of-Distribution 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