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 331–340 of 4856 papers

Title	Date	Tasks	Status	Hype
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow	May 16, 2023	Anomaly DetectionOut-of-Distribution Detection	CodeCode Available	1
A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	Oct 18, 2023	Anomaly DetectionImputation	CodeCode Available	1
Coniferest: a complete active anomaly detection framework	Oct 22, 2024	Anomaly DetectionOutlier Detection	CodeCode Available	1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images	Mar 5, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data	Jul 24, 2022	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection	Mar 10, 2022	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	1
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving	Dec 6, 2024	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection	Jul 17, 2023	Anomaly DetectionGraph Learning	CodeCode Available	1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection	Mar 27, 2020	Anomaly DetectionBinary Classification	CodeCode Available	1
DAGAD: Data Augmentation for Graph Anomaly Detection	Oct 18, 2022	Anomaly DetectionData Augmentation	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