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 2131–2140 of 4856 papers

Title	Date	Tasks	Status	Hype
AGAD: Adversarial Generative Anomaly Detection	Apr 9, 2023	Anomaly DetectionDiversity	—Unverified	0
KeyDetect --Detection of anomalies and user based on Keystroke Dynamics	Apr 8, 2023	Anomaly DetectionMulti-class Classification	—Unverified	0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining	Apr 7, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder	Apr 7, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
What makes a good data augmentation for few-shot unsupervised image anomaly detection?	Apr 6, 2023	Anomaly DetectionData Augmentation	—Unverified	0
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management	Apr 6, 2023	Anomaly DetectionManagement	—Unverified	0
Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems	Apr 6, 2023	Anomaly DetectionNovelty Detection	—Unverified	0
Anomaly Detection via Gumbel Noise Score Matching	Apr 6, 2023	Anomaly DetectionSegmentation	—Unverified	0
Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction	Apr 5, 2023	Anomaly DetectionDiversity	CodeCode Available	1
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection	Apr 5, 2023	Anomaly DetectionDecoder	—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