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 3421–3430 of 4856 papers

Title	Date	Tasks	Status	Hype
Brittle Features May Help Anomaly Detection	Apr 21, 2021	Anomaly DetectionKnowledge Distillation	—Unverified	0
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams	Apr 21, 2021	Anomaly DetectionDrift Detection	CodeCode Available	1
An Efficient Approach for Anomaly Detection in Traffic Videos	Apr 20, 2021	Anomaly DetectionChange Detection	—Unverified	0
Fine-grained Anomaly Detection via Multi-task Self-Supervision	Apr 20, 2021	Anomaly DetectionSelf-Supervised Learning	—Unverified	0
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
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series	Apr 19, 2021	Anomaly DetectionTime Series	—Unverified	0
Autoencoders for unsupervised anomaly detection in high energy physics	Apr 19, 2021	Anomaly DetectionJet Tagging	—Unverified	0
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands	Apr 17, 2021	Anomaly Detection	—Unverified	0

Show:10 25 50

← PrevPage 343 of 486Next →

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