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 811–820 of 4856 papers

Title	Date	Tasks	Status	Hype
Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series	Oct 2, 2024	Anomaly DetectionTime Series	—Unverified	0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks	Oct 2, 2024	Anomaly DetectionGraph Learning	CodeCode Available	0
Uncertainty-aware Human Mobility Modeling and Anomaly Detection	Oct 2, 2024	Anomaly DetectionDecision Making	—Unverified	0
LEGO: Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion	Oct 2, 2024	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection	Oct 2, 2024	Anomaly DetectionPhilosophy	—Unverified	0
Interactive Explainable Anomaly Detection for Industrial Settings	Oct 1, 2024	Anomaly Detection	—Unverified	0
AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors	Oct 1, 2024	Anomaly DetectionAttribute	—Unverified	0
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection	Oct 1, 2024	3D Anomaly DetectionAnomaly Detection	CodeCode Available	2
RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations	Oct 1, 2024	Anomaly DetectionData Augmentation	CodeCode Available	1
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework	Oct 1, 2024	Anomaly Detection	—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