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 771–780 of 4856 papers

Title	Date	Tasks	Status	Hype
Revisited Large Language Model for Time Series Analysis through Modality Alignment	Oct 16, 2024	Anomaly DetectionImputation	—Unverified	0
A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation	Oct 15, 2024	Anomaly DetectionRed Teaming	—Unverified	0
SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detection	Oct 15, 2024	3D Anomaly Detection3DGS	CodeCode Available	0
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study	Oct 15, 2024	Anomaly DetectionFederated Learning	—Unverified	0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection	Oct 15, 2024	Anomaly DetectionContrastive Learning	—Unverified	0
PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge	Oct 15, 2024	Anomaly Detection	CodeCode Available	0
TAAD: Time-varying adversarial anomaly detection in dynamic graphs	Oct 14, 2024	Anomaly Detection	—Unverified	0
LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space	Oct 14, 2024	Anomaly DetectionSelf-Supervised Learning	—Unverified	0
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection	Oct 14, 2024	Anomaly DetectionPrompt Learning	CodeCode Available	1
AI-based particle track identification in scintillating fibres read out with imaging sensors	Oct 14, 2024	Anomaly Detection	—Unverified	0

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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