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 811–820 of 4856 papers

Title	Date	Tasks	Status	Hype
Automating Outlier Detection via Meta-Learning	Sep 22, 2020	Anomaly DetectionAutoML	CodeCode Available	1
Real-Time Anomaly Detection in Edge Streams	Sep 17, 2020	Anomaly DetectionAnomaly Detection in Edge Streams	CodeCode Available	1
MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams	Sep 17, 2020	Anomaly DetectionGroup Anomaly Detection	CodeCode Available	1
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning	Sep 16, 2020	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data	Sep 15, 2020	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1
Generator Versus Segmentor: Pseudo-healthy Synthesis	Sep 12, 2020	Anomaly DetectionGenerative Adversarial Network	CodeCode Available	1
Understanding Coarsening for Embedding Large-Scale Graphs	Sep 10, 2020	Anomaly DetectionGraph Embedding	CodeCode Available	1
PySAD: A Streaming Anomaly Detection Framework in Python	Sep 5, 2020	Anomaly Detection	CodeCode Available	1
Multivariate Time-series Anomaly Detection via Graph Attention Network	Sep 4, 2020	Anomaly DetectionGraph Attention	CodeCode Available	1
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise	Aug 29, 2020	Anomaly Detection	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