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 261–270 of 4856 papers

Title	Date	Tasks	Status	Hype
ZClip: Adaptive Spike Mitigation for LLM Pre-Training	Apr 3, 2025	Anomaly DetectionLarge Language Model	CodeCode Available	2
Beyond Conventional Transformers: The Medical X-ray Attention (MXA) Block for Improved Multi-Label Diagnosis Using Knowledge Distillation	Apr 3, 2025	Anomaly DetectionKnowledge Distillation	CodeCode Available	0
CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection	Apr 3, 2025	Anomaly DetectionGraph Anomaly Detection	—Unverified	0
Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection	Apr 2, 2025	Anomaly DetectionEarth Observation	—Unverified	0
What is AI, what is it not, how we use it in physics and how it impacts... you	Apr 2, 2025	Anomaly DetectionMisconceptions	—Unverified	0
Anomaly Detection for Hybrid Butterfly Subspecies via Probability Filtering	Apr 2, 2025	Anomaly Detection	CodeCode Available	0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection	Apr 1, 2025	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting	Mar 31, 2025	Anomaly DetectionMultivariate Time Series Forecasting	CodeCode Available	0
Enhancing Time Series Forecasting with Fuzzy Attention-Integrated Transformers	Mar 31, 2025	Anomaly DetectionDecision Making	CodeCode Available	0
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks	Mar 31, 2025	Anomaly DetectionGraph Attention	—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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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