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 2811–2820 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs	Apr 28, 2022	Anomaly DetectionGenerative Adversarial Network	CodeCode Available	1
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection	Apr 27, 2022	Anomaly DetectionFault Detection	—Unverified	0
IRC-safe Graph Autoencoder for unsupervised anomaly detection	Apr 26, 2022	Anomaly DetectionSensitivity	—Unverified	0
Novel Applications for VAE-based Anomaly Detection Systems	Apr 26, 2022	Anomaly Detection	—Unverified	0
Topological Data Analysis for Anomaly Detection in Host-Based Logs	Apr 25, 2022	Anomaly DetectionTopological Data Analysis	—Unverified	0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images	Apr 24, 2022	Anomaly DetectionDefect Detection	—Unverified	0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV	Apr 23, 2022	Anomaly DetectionContinual Learning	—Unverified	0
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection	Apr 23, 2022	Anomaly DetectionAnomaly Localization	—Unverified	0
NLP Based Anomaly Detection for Categorical Time Series	Apr 22, 2022	Anomaly DetectionTime Series	—Unverified	0
Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks	Apr 21, 2022	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	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