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 381–390 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network	Apr 19, 2021	Anomaly DetectionOutlier Detection	CodeCode Available	1	5
Beyond Individual Input for Deep Anomaly Detection on Tabular Data	May 24, 2023	Anomaly Detection	CodeCode Available	1	5
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection	Oct 19, 2023	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1	5
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models	May 13, 2024	Anomaly DetectionEdge Detection	CodeCode Available	1	5
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling	Feb 6, 2019	Anomaly DetectionAttribute	CodeCode Available	1	5
Blind Localization and Clustering of Anomalies in Textures	Apr 18, 2024	Anomaly DetectionAnomaly Localization	CodeCode Available	1	5
AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection	Apr 16, 2025	Anomaly DetectionLarge Language Model	CodeCode Available	1	5
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	Jun 14, 2021	Anomaly DetectionDeep Learning	CodeCode Available	1	5
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning	Dec 17, 2024	Anomaly Detection	CodeCode Available	1	5
Detecting Anomalies within Time Series using Local Neural Transformations	Feb 8, 2022	Anomaly DetectionEpidemiology	CodeCode Available	1	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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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