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 571–580 of 4856 papers

Title	Date	Tasks	Status	Hype
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation	Dec 20, 2024	Anomaly DetectionData Augmentation	CodeCode Available	0
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India	Dec 20, 2024	Anomaly DetectionDecision Making	—Unverified	0
A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection	Dec 19, 2024	Anomaly DetectionManagement	—Unverified	0
Robust PCA Based on Adaptive Weighted Least Squares and Low-Rank Matrix Factorization	Dec 19, 2024	Anomaly DetectionComputational Efficiency	—Unverified	0
Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties	Dec 19, 2024	Anomaly DetectionObject	CodeCode Available	2
Tests for model misspecification in simulation-based inference: from local distortions to global model checks	Dec 19, 2024	Anomaly Detectionmodel	CodeCode Available	2
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection	Dec 19, 2024	Anomaly DetectionEEG	CodeCode Available	1
Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware	Dec 19, 2024	Anomaly DetectionPrediction	—Unverified	0
Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation	Dec 18, 2024	Anomaly Detection	CodeCode Available	0
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities	Dec 18, 2024	Anomaly DetectionModel Selection	—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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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