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 3521–3530 of 4856 papers

Title	Date	Tasks	Status
One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map	Jun 30, 2021	Anomaly DetectionDefect Detection	—Unverified
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol	Jun 30, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net	Jun 29, 2021	Anomaly Detection	—Unverified
A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network	Jun 28, 2021	Anomaly DetectionSelf-Learning	—Unverified
Approximate Maximum Halfspace Discrepancy	Jun 25, 2021	Anomaly Detection	—Unverified
Generalized One-Class Learning Using Pairs of Complementary Classifiers	Jun 24, 2021	Anomaly Detection	—Unverified
Task-agnostic Continual Learning with Hybrid Probabilistic Models	Jun 24, 2021	Anomaly DetectionContinual Learning	—Unverified
A new Video Synopsis Based Approach Using Stereo Camera	Jun 23, 2021	Anomaly DetectionVideo Synopsis	—Unverified
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection	Jun 23, 2021	Anomaly DetectionGaussian Processes	—Unverified
Detecting Anomalous User Behavior in Remote Patient Monitoring	Jun 22, 2021	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