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 2941–2950 of 4856 papers

Title	Date	Tasks	Status
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes	Sep 21, 2022	Anomaly DetectionDeep Learning	—Unverified
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics	Sep 21, 2022	Anomaly DetectionBenchmarking	—Unverified
Learning Acceptance Regions for Many Classes with Anomaly Detection	Sep 20, 2022	Anomaly DetectionClassification	—Unverified
Collaborative Anomaly Detection	Sep 20, 2022	Anomaly DetectionDensity Estimation	—Unverified
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model	Sep 19, 2022	Anomaly DetectionDensity Ratio Estimation	—Unverified
Infrared: A Meta Bug Detector	Sep 18, 2022	Anomaly Detection	—Unverified
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks	Sep 16, 2022	Anomaly Detection	—Unverified
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning	Sep 16, 2022	Anomaly DetectionTransfer Learning	—Unverified
Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning	Sep 15, 2022	Anomaly DetectionDictionary Learning	—Unverified
Analytics and Machine Learning Powered Wireless Network Optimization and Planning	Sep 14, 2022	Anomaly Detection	—Unverified

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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