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 1221–1230 of 4856 papers

Title	Date	Tasks	Status
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning	Mar 5, 2025	Anomaly DetectionClassification	—Unverified
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions	Mar 5, 2025	Anomaly DetectionVisual Grounding	—Unverified
DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent Features	Mar 5, 2025	Anomaly DetectionData Augmentation	CodeCode Available
AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates	Mar 5, 2025	Anomaly DetectionDefect Detection	—Unverified
World Models for Anomaly Detection during Model-Based Reinforcement Learning Inference	Mar 4, 2025	Anomaly DetectionModel-based Reinforcement Learning	—Unverified
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD	Mar 4, 2025	Anomaly DetectionIntrusion Detection	—Unverified
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning	Mar 4, 2025	Anomaly DetectionComputed Tomography (CT)	—Unverified
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration	Mar 4, 2025	Anomaly DetectionRAG	—Unverified
Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection	Mar 4, 2025	Anomaly DetectionContinual Learning	—Unverified
Anomaly detection in non-stationary videos using time-recursive differencing network based prediction	Mar 4, 2025	Anomaly DetectionOptical Flow Estimation	—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