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 1541–1550 of 4856 papers

Title	Date	Tasks	Status	Score
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection	May 9, 2025	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	5
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning	Feb 5, 2021	Anomaly Detectionregression	CodeCode Available	5
Enhancing Interpretability and Generalizability in Extended Isolation Forests	Oct 9, 2023	Anomaly DetectionFeature Importance	CodeCode Available	5
Explainable Anomaly Detection for Industrial Control System Cybersecurity	May 4, 2022	Anomaly DetectionExplainable artificial intelligence	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
Approaches Toward Physical and General Video Anomaly Detection	Dec 14, 2021	Anomaly DetectionDensity Estimation	CodeCode Available	5
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT	Aug 17, 2022	Anomaly DetectionUnsupervised 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
Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks	Jul 2, 2024	Activity PredictionAnomaly Detection	CodeCode Available	5
Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing	Nov 22, 2024	Anomaly Detection	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