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 741–750 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection for Solder Joints Using β-VAE	Apr 24, 2021	Anomaly DetectionFeature Engineering	CodeCode Available	1
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning	Apr 24, 2021	Active LearningAnomaly Detection	CodeCode Available	1
An End-to-End Computer Vision Methodology for Quantitative Metallography	Apr 22, 2021	Anomaly DetectionImage Inpainting	CodeCode Available	1
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams	Apr 21, 2021	Anomaly DetectionDrift Detection	CodeCode Available	1
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization	Apr 20, 2021	Anomaly Detection	CodeCode Available	1
What is Wrong with One-Class Anomaly Detection?	Apr 20, 2021	Anomaly Detection	CodeCode Available	1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network	Apr 19, 2021	Anomaly DetectionOutlier Detection	CodeCode Available	1
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning	Apr 15, 2021	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
ADNet: Temporal Anomaly Detection in Surveillance Videos	Apr 14, 2021	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Learning Normal Dynamics in Videos with Meta Prototype Network	Apr 14, 2021	Anomaly DetectionMeta-Learning	CodeCode Available	1

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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