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 981–990 of 4856 papers

Title	Date	Tasks	Status	Hype
Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability	Aug 1, 2024	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	0
Small Object Few-shot Segmentation for Vision-based Industrial Inspection	Jul 31, 2024	Anomaly DetectionFew-Shot Semantic Segmentation	CodeCode Available	1
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT	Jul 30, 2024	Anomaly DetectionTime Series	—Unverified	0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling	Jul 30, 2024	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning	Jul 29, 2024	Anomaly DetectionBenchmarking	—Unverified	0
Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Data	Jul 29, 2024	Anomaly Detection	—Unverified	0
Can I trust my anomaly detection system? A case study based on explainable AI	Jul 29, 2024	Anomaly Detection	CodeCode Available	0
Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models	Jul 29, 2024	Anomaly DetectionLanguage Modeling	—Unverified	0
Textile Anomaly Detection: Evaluation of the State-of-the-Art for Automated Quality Inspection of Carpet	Jul 26, 2024	Anomaly DetectionCPU	—Unverified	0
Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection	Jul 26, 2024	Anomaly DetectionDeep Learning	—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