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 4191–4200 of 4856 papers

Title	Date	Tasks	Status	Hype
Fault-Diagnosing SLAM for Varying Scale Change Detection	Sep 16, 2019	Anomaly DetectionChange Detection	—Unverified	0
No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection	Sep 16, 2019	Anomaly Detection	—Unverified	0
LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory	Sep 13, 2019	Anomaly DetectionPrediction	—Unverified	0
Perceptual Image Anomaly Detection	Sep 12, 2019	Anomaly DetectionImage Reconstruction	CodeCode Available	1
Anomaly Detection with Inexact Labels	Sep 11, 2019	Anomaly DetectionMultiple Instance Learning	—Unverified	0
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs	Sep 10, 2019	Anomaly DetectionTAG	—Unverified	0
A Flexible Framework for Anomaly Detection via Dimensionality Reduction	Sep 9, 2019	Active LearningAnomaly Detection	CodeCode Available	0
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise	Sep 9, 2019	Anomaly Detection	—Unverified	0
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection	Sep 8, 2019	Anomaly DetectionRaw reconstruction	—Unverified	0
Image anomaly detection with capsule networks and imbalanced datasets	Sep 6, 2019	Anomaly Detection	CodeCode Available	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	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