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 851–860 of 4856 papers

Title	Date	Tasks	Status	Hype
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems	Sep 21, 2024	Anomaly Detection	—Unverified	0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection	Sep 21, 2024	Anomaly DetectionContrastive Learning	—Unverified	0
Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis	Sep 20, 2024	Anomaly DetectionFault Diagnosis	CodeCode Available	1
Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models	Sep 20, 2024	AnatomyAnomaly Detection	—Unverified	0
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring	Sep 20, 2024	Anomaly DetectionMetric Learning	—Unverified	0
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework	Sep 20, 2024	Anomaly DetectionSpecificity	CodeCode Available	2
Towards Unbiased Evaluation of Time-series Anomaly Detector	Sep 19, 2024	Anomaly DetectionFairness	CodeCode Available	0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space	Sep 19, 2024	Anomaly DetectionBinary Classification	—Unverified	0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability	Sep 19, 2024	Anomaly DetectionData Augmentation	—Unverified	0
Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User Anomalies	Sep 19, 2024	Anomaly DetectionGraph Embedding	CodeCode Available	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