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 971–980 of 4856 papers

Title	Date	Tasks	Status	Score
AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset	Feb 2, 2021	Anomaly Detection	CodeCode Available	5
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights	Mar 14, 2023	Anomaly DetectionContinual Learning	CodeCode Available	5
Automatic Anomaly Detection in the Cloud Via Statistical Learning	Apr 24, 2017	Anomaly DetectionTime Series	CodeCode Available	5
Lightweight Collaborative Anomaly Detection for the IoT using Blockchain	Jun 18, 2020	Anomaly Detection	CodeCode Available	5
Latent Space Autoregression for Novelty Detection	Jul 4, 2018	Anomaly DetectionNovelty Detection	CodeCode Available	5
A Uniform Framework for Anomaly Detection in Deep Neural Networks	Oct 6, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	5
Learning Deep Features for One-Class Classification	Jan 16, 2018	Anomaly DetectionDescriptive	CodeCode Available	5
Anomaly Detection for Hybrid Butterfly Subspecies via Probability Filtering	Apr 2, 2025	Anomaly Detection	CodeCode Available	5
Learning normal asymmetry representations for homologous brain structures	Jun 27, 2023	Anomaly DetectionRepresentation Learning	CodeCode Available	5
A Flexible Framework for Anomaly Detection via Dimensionality Reduction	Sep 9, 2019	Active LearningAnomaly 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