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 1511–1520 of 4856 papers

Title	Date	Tasks	Status
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review	Mar 7, 2022	Anomaly DetectionAutonomous Vehicles	—Unverified
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data	Jun 28, 2019	Anomaly DetectionMulti-Task Learning	—Unverified
Deep Nearest Neighbor Anomaly Detection	Feb 24, 2020	Anomaly Detection	—Unverified
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection	Sep 26, 2024	Anomaly DetectionDomain Adaptation	—Unverified
AnomMAN: Detect Anomaly on Multi-view Attributed Networks	Jan 8, 2022	Anomaly Detection	—Unverified
Application Of ADNN For Background Subtraction In Smart Surveillance System	Dec 31, 2022	Anomaly DetectionMotion Detection	—Unverified
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks	Mar 16, 2018	Anomaly DetectionIntrusion Detection	—Unverified
Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks	May 13, 2025	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

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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