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 491–500 of 4856 papers

Title	Date	Tasks	Status	Hype
Score Combining for Contrastive OOD Detection	Jan 21, 2025	Anomaly DetectionContrastive Learning	—Unverified	0
TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection	Jan 21, 2025	Anomaly DetectionMisinformation	—Unverified	0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection	Jan 21, 2025	Anomaly DetectionAnomaly Segmentation	—Unverified	0
Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review	Jan 20, 2025	Anomaly Detection	—Unverified	0
A Survey of World Models for Autonomous Driving	Jan 20, 2025	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model	Jan 20, 2025	Anomaly DetectionOutlier Detection	—Unverified	0
A Survey on Diffusion Models for Anomaly Detection	Jan 20, 2025	Anomaly DetectionComputational Efficiency	CodeCode Available	2
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization	Jan 17, 2025	Anomaly DetectionImage-text matching	CodeCode Available	2
ComplexVAD: Detecting Interaction Anomalies in Video	Jan 16, 2025	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities	Jan 16, 2025	Anomaly DetectionSynthetic Data Generation	—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