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 3051–3060 of 4856 papers

Title	Date	Tasks	Status
Intrinsic Anomaly Detection for Multi-Variate Time Series	Jun 29, 2022	Anomaly DetectionNavigate	—Unverified
Framing Algorithmic Recourse for Anomaly Detection	Jun 29, 2022	Anomaly DetectionDecoder	—Unverified
Generative Anomaly Detection for Time Series Datasets	Jun 28, 2022	Anomaly DetectionDensity Estimation	—Unverified
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning	Jun 27, 2022	Anomaly DetectionSensitivity	—Unverified
Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange	Jun 26, 2022	Anomaly DetectionDecoder	—Unverified
Self-Supervised Training with Autoencoders for Visual Anomaly Detection	Jun 23, 2022	Anomaly DetectionDenoising	—Unverified
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark	Jun 23, 2022	Anomaly Detectionimage-classification	CodeCode Available
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching	Jun 22, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs	Jun 21, 2022	Anomaly DetectionBenchmarking	CodeCode Available
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient	Jun 21, 2022	Anomaly DetectionSemi-supervised Anomaly Detection	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