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 1551–1560 of 4856 papers

Title	Date	Tasks	Status	Score
Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing	Nov 22, 2024	Anomaly Detection	CodeCode Available	5
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET	Jan 29, 2024	Anomaly DetectionImage Reconstruction	CodeCode Available	5
Evaluating Language Models For Threat Detection in IoT Security Logs	Jul 3, 2025	Anomaly Detection	CodeCode Available	5
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation	Nov 30, 2018	Anomaly DetectionAutonomous Driving	CodeCode Available	5
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study	Jan 30, 2024	Anomaly DetectionDataset Generation	CodeCode Available	5
Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection	Apr 20, 2020	Anomaly Detection	CodeCode Available	5
Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks	Jan 10, 2025	Anomaly DetectionBenchmarking	CodeCode Available	5
Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series	Apr 15, 2024	Anomaly DetectionCausal Discovery	CodeCode Available	5
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection	Jun 29, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available	5
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study	Feb 11, 2024	Anomaly DetectionBenchmarking	CodeCode Available	5

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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