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 4221–4230 of 4856 papers

Title	Date	Tasks	Status	Hype
Detecting semantic anomalies	Aug 13, 2019	Anomaly DetectionMulti-Task Learning	CodeCode Available	0
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection	Aug 12, 2019	Action DetectionActivity Detection	—Unverified	0
Anomaly Detection in High Dimensional Data	Aug 12, 2019	Anomaly DetectionFeature Engineering	CodeCode Available	0
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks	Aug 11, 2019	Anomaly DetectionDensity Estimation	—Unverified	0
Deep Structured Cross-Modal Anomaly Detection	Aug 11, 2019	Anomaly Detection	—Unverified	0
Transcriptional Response of SK-N-AS Cells to Methamidophos	Aug 11, 2019	Anomaly DetectionCausal Inference	—Unverified	0
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection	Aug 9, 2019	Anomaly DetectionFault Diagnosis	CodeCode Available	1
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks	Aug 8, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Task-Oriented Optimal Sequencing of Visualization Charts	Aug 7, 2019	Anomaly DetectionReinforcement Learning	—Unverified	0
Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization	Aug 6, 2019	Anomaly DetectionDiagnostic	—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	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