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 4451–4460 of 4856 papers

Title	Date	Tasks	Status
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection	Mar 18, 2025	Anomaly DetectionKnowledge Distillation	CodeCode Available
gen2Out: Detecting and Ranking Generalized Anomalies	Sep 6, 2021	Anomaly DetectionEEG	CodeCode Available
Anomaly Detection using One-Class Neural Networks	Feb 18, 2018	Anomaly Detection	CodeCode Available
neuralGAM: Explainable generalized additive neural networks with independent neural network training	Jul 8, 2024	Additive modelsAnomaly Detection	CodeCode Available
Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect Classification	May 28, 2024	Anomaly DetectionClassification	CodeCode Available
β-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation	Mar 26, 2025	Anomaly DetectionManagement	CodeCode Available
Beyond the Benchmark: Detecting Diverse Anomalies in Videos	Oct 3, 2023	Action RecognitionAnomaly Detection	CodeCode Available
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification	Aug 28, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection	Mar 15, 2019	Anomaly Detection	CodeCode Available
Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through Visual Prototype and Harmonization	Jan 1, 2025	Anomaly Detection	CodeCode Available

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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