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 51–60 of 4856 papers

Title	Date	Tasks	Status	Hype
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain	Jun 12, 2025	Anomaly Detection	CodeCode Available	0
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information	Jun 12, 2025	Anomaly DetectionDiagnostic	—Unverified	0
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization	Jun 11, 2025	Anomaly DetectionImage Reconstruction	—Unverified	0
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning	Jun 11, 2025	Anomaly DetectionFederated Learning	—Unverified	0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation	Jun 10, 2025	Anomaly DetectionData Augmentation	—Unverified	0
Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications	Jun 10, 2025	Anomaly DetectionFederated Learning	—Unverified	0
PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies	Jun 10, 2025	Adversarial RobustnessAnomaly Detection	CodeCode Available	1
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization	Jun 10, 2025	Anomaly DetectionContrastive Learning	—Unverified	0
HomographyAD: Deep Anomaly Detection Using Self Homography Learning	Jun 10, 2025	Anomaly DetectionHomography Estimation	—Unverified	0
MAMBO: High-Resolution Generative Approach for Mammography Images	Jun 10, 2025	Anomaly DetectionImage Generation	—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	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