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 2251–2260 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Anomaly Detection under Labeling Budget Constraints	Feb 15, 2023	Anomaly DetectionFraud Detection	CodeCode Available	0
Ultrafast single-channel machine vision based on neuro-inspired photonic computing	Feb 15, 2023	Anomaly Detection	—Unverified	0
Energy Transformer	Feb 14, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes	Feb 14, 2023	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework	Feb 14, 2023	Anomaly DetectionGeochemistry	—Unverified	0
Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention	Feb 14, 2023	Anomaly Detection	CodeCode Available	1
Explainable Anomaly Detection in Images and Videos: A Survey	Feb 13, 2023	Anomaly DetectionSurvey	CodeCode Available	1
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics	Feb 13, 2023	Anomaly DetectionOne-Class Classification	—Unverified	0
Deep Orthogonal Hypersphere Compression for Anomaly Detection	Feb 13, 2023	Anomaly Detection	CodeCode Available	1
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis	Feb 13, 2023	Anomaly DetectionClustering	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	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