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 2731–2740 of 4856 papers

Title	Date	Tasks	Status
Client-Specific Anomaly Detection for Face Presentation Attack Detection	Jul 2, 2018	Anomaly DetectionDecision Making	—Unverified
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation	Jun 27, 2024	Anomaly ClassificationAnomaly Detection	—Unverified
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection	Nov 1, 2023	Anomaly DetectionLanguage Modeling	—Unverified
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing	May 16, 2025	Anomaly DetectionCloud Computing	—Unverified
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms	Nov 5, 2021	Anomaly DetectionCloud Computing	—Unverified
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series	May 30, 2025	Anomaly DetectionTime Series	—Unverified
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos	Mar 25, 2022	Anomaly DetectionClustering	—Unverified
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations	Feb 1, 2018	Anomaly DetectionClustering	—Unverified
Clustering-based Anomaly Detection for microservices	Oct 4, 2018	Anomaly DetectionCloud Computing	—Unverified
Clustering Driven Deep Autoencoder for Video Anomaly Detection	Aug 1, 2020	Anomaly DetectionClustering	—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