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 2791–2800 of 4856 papers

Title	Date	Tasks	Status
Context-aware Domain Adaptation for Time Series Anomaly Detection	Apr 15, 2023	Anomaly DetectionDeep Reinforcement Learning	—Unverified
Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition	May 3, 2025	Anomaly Detection	—Unverified
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization	Jun 10, 2025	Anomaly DetectionContrastive Learning	—Unverified
Context-Aware Trajectory Anomaly Detection	Oct 24, 2024	Anomaly DetectionDecision Making	—Unverified
Context-aware Video Anomaly Detection in Long-Term Datasets	Apr 11, 2024	Anomaly DetectionContrastive Learning	—Unverified
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance	Apr 14, 2021	Anomaly DetectionContextual Anomaly Detection	—Unverified
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models	Mar 17, 2022	Anomaly DetectionGraph Embedding	—Unverified
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection	Dec 14, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Contextual Affinity Distillation for Image Anomaly Detection	Jul 6, 2023	Anomaly DetectionKnowledge Distillation	—Unverified
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing	Apr 15, 2020	Anomaly DetectionEdge-computing	—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