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 4571–4580 of 4856 papers

Title	Date	Tasks	Status
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks	Jan 30, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection	Jun 29, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection	Apr 19, 2025	Anomaly DetectionContrastive Learning	CodeCode Available
DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection	Feb 17, 2025	Anomaly Detection	CodeCode Available
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs	Jun 21, 2022	Anomaly DetectionBenchmarking	CodeCode Available
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path Signature	Jun 5, 2020	Anomaly DetectionTime Series	CodeCode Available
Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System	Jul 28, 2023	Anomaly DetectionAutonomous Driving	CodeCode Available
Online Self-Evolving Anomaly Detection in Cloud Computing Environments	Nov 16, 2021	Anomaly DetectionCloud Computing	CodeCode Available
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems	May 12, 2025	Anomaly DetectionContrastive Learning	CodeCode Available
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks	Sep 22, 2022	Anomaly DetectionImputation	CodeCode Available

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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