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 2281–2290 of 4856 papers

Title	Date	Tasks	Status	Hype
An optimization method for out-of-distribution anomaly detection models	Feb 2, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs	Feb 2, 2023	Anomaly DetectionChange Point Detection	CodeCode Available	1
Graph Anomaly Detection in Time Series: A Survey	Jan 31, 2023	Anomaly DetectionGraph Anomaly Detection	—Unverified	0
Real-Time Outlier Detection with Dynamic Process Limits	Jan 31, 2023	Anomaly DetectionOutlier Detection	—Unverified	0
LogAI: A Library for Log Analytics and Intelligence	Jan 31, 2023	Anomaly DetectionLog Parsing	CodeCode Available	2
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing	Jan 31, 2023	Anomaly DetectionContinual Learning	CodeCode Available	1
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation	Jan 30, 2023	Anomaly DetectionKnowledge Distillation	CodeCode Available	0
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays	Jan 30, 2023	Anomaly DetectionRepresentation Learning	CodeCode Available	1
Approximating DTW with a convolutional neural network on EEG data	Jan 30, 2023	Anomaly DetectionComputational Efficiency	—Unverified	0
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series	Jan 30, 2023	Anomaly DetectionState Space Models	—Unverified	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	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