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 991–1000 of 4856 papers

Title	Date	Tasks	Status	Hype
Separating Novel Features for Logical Anomaly Detection: A Straightforward yet Effective Approach	Jul 25, 2024	Anomaly DetectionKnowledge Distillation	—Unverified	0
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning	Jul 24, 2024	Anomaly DetectionIn-Context Learning	CodeCode Available	0
Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection	Jul 24, 2024	Anomaly DetectionFraud Detection	—Unverified	0
Looking at Model Debiasing through the Lens of Anomaly Detection	Jul 24, 2024	Anomaly Detection	CodeCode Available	0
When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection	Jul 24, 2024	Anomaly Detection	—Unverified	0
Hyperbolic embedding of brain networks detects regions disrupted by neurodegeneration in Alzheimer's disease	Jul 23, 2024	Anomaly Detection	—Unverified	0
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection	Jul 22, 2024	Anomaly DetectionLanguage Modeling	CodeCode Available	3
Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond	Jul 22, 2024	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention	Jul 22, 2024	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis	Jul 22, 2024	Anomaly DetectionObject	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