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 41–50 of 4856 papers

Title	Date	Tasks	Status	Hype
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies	Jun 16, 2025	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better	Jun 15, 2025	Anomaly Detection	CodeCode Available	1
SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models	Jun 15, 2025	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles	Jun 15, 2025	Anomaly DetectionAnomaly Forecasting	—Unverified	0
Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation	Jun 13, 2025	Anomaly DetectionBenchmarking	—Unverified	0
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation	Jun 13, 2025	Anomaly DetectionClustering	CodeCode Available	1
Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs	Jun 13, 2025	Anomaly DetectionTime Series	CodeCode Available	0
Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection	Jun 13, 2025	Anomaly DetectionDecision Making	CodeCode Available	1
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective	Jun 13, 2025	Anomaly DetectionDimensionality Reduction	CodeCode Available	0
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping	Jun 13, 2025	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1

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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