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 3071–3080 of 4856 papers

Title	Date	Tasks	Status
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation	Aug 17, 2022	Anomaly DetectionClassification	—Unverified
Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets	Aug 19, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel	Aug 1, 2023	Anomaly DetectionQuantum Machine Learning	—Unverified
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs	Jan 25, 2025	Anomaly DetectionGraph Anomaly Detection	—Unverified
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models	Sep 27, 2024	Anomaly DetectionKnowledge Distillation	—Unverified
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion	Dec 5, 2023	Anomaly DetectionDiversity	—Unverified
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders	Dec 2, 2019	Anomaly DetectionFault Diagnosis	—Unverified
Semi-supervised learning via DQN for log anomaly detection	Jan 6, 2024	Anomaly DetectionDeep Reinforcement Learning	—Unverified
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery	Dec 1, 2021	Anomaly DetectionFault Detection	—Unverified
Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection	Apr 5, 2021	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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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