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 1261–1270 of 4856 papers

Title	Date	Tasks	Status
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning	Jun 5, 2020	Anomaly Detectionimage-classification	—Unverified
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition	Sep 18, 2021	Anomaly DetectionTime Series	—Unverified
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems	Oct 16, 2018	Anomaly DetectionAutonomous Vehicles	—Unverified
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination	Sep 18, 2023	Anomaly DetectionOne-Class Classification	—Unverified
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection	Feb 9, 2022	Anomaly DetectionFederated Learning	—Unverified
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models	Jan 24, 2025	Anomaly DetectionTime Series	—Unverified
An Introduction to Autoencoders	Jan 11, 2022	Anomaly DetectionDenoising	—Unverified
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection	Apr 16, 2024	Anomaly DetectionIntrusion Detection	—Unverified
ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk Assessment	Apr 24, 2024	Anomaly Detection	—Unverified
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach	Jul 19, 2020	Anomaly DetectionFederated Learning	—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