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 981–990 of 4856 papers

Title	Date	Tasks	Status	Score
A Uniform Framework for Anomaly Detection in Deep Neural Networks	Oct 6, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	5
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection	Nov 15, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	5
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning	Jul 24, 2024	Anomaly DetectionIn-Context Learning	CodeCode Available	5
Large Models in Dialogue for Active Perception and Anomaly Detection	Jan 27, 2025	Anomaly DetectionQuestion Answering	CodeCode Available	5
A Flexible Framework for Anomaly Detection via Dimensionality Reduction	Sep 9, 2019	Active LearningAnomaly Detection	CodeCode Available	5
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images	Apr 15, 2025	AnatomyAnomaly Detection	CodeCode Available	5
Language-Assisted Feature Transformation for Anomaly Detection	Mar 3, 2025	Anomaly Detection	CodeCode Available	5
Kinematic Detection of Anomalies in Human Trajectory Data	Sep 27, 2024	Anomaly Detection	CodeCode Available	5
Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes	Jan 25, 2025	Anomaly DetectionDensity Estimation	CodeCode Available	5
Known Unknowns: Uncertainty Quality in Bayesian Neural Networks	Dec 5, 2016	Anomaly DetectionKnown Unknowns	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