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 1531–1540 of 4856 papers

Title	Date	Tasks	Status
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection	Jul 18, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified
Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction	Sep 20, 2021	Anomaly Detection	—Unverified
Deep Structured Cross-Modal Anomaly Detection	Aug 11, 2019	Anomaly Detection	—Unverified
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection	Oct 13, 2023	Anomaly DetectionDenoising	—Unverified
Anomize: Better Open Vocabulary Video Anomaly Detection	Mar 23, 2025	Anomaly DetectionVideo Anomaly Detection	—Unverified
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction	Dec 21, 2024	Anomaly DetectionContrastive Learning	—Unverified
An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction	Feb 5, 2023	Anomaly DetectionLoad Forecasting	—Unverified
Deep unsupervised anomaly detection	Jan 5, 2021	Anomaly DetectionClustering	—Unverified
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework	Dec 9, 2020	Anomaly Detection	—Unverified
AAD: Adaptive Anomaly Detection through traffic surveillance videos	Aug 29, 2018	Action DetectionActivity Detection	—Unverified

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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