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 1521–1530 of 4856 papers

Title	Date	Tasks	Status
Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network	Nov 10, 2019	Anomaly Detection	—Unverified
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection	Jun 8, 2021	Anomaly DetectionCompressive Sensing	—Unverified
Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems	Mar 25, 2021	Anomaly Detection	—Unverified
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection	Jul 18, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection	Oct 13, 2023	Anomaly DetectionDenoising	—Unverified
Deep Representation Learning for Social Network Analysis	Apr 18, 2019	Anomaly DetectionAttribute	—Unverified
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review	Oct 22, 2021	Anomaly DetectionSystematic Literature Review	—Unverified
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market	Aug 31, 2023	Anomaly DetectionFraud Detection	—Unverified
Applications of Machine Learning to the Identification of Anomalous ER Claims	Jun 16, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Anomize: Better Open Vocabulary Video Anomaly Detection	Mar 23, 2025	Anomaly DetectionVideo Anomaly 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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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