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 1071–1080 of 4856 papers

Title	Date	Tasks	Status	Score
Inferring Networks From Random Walk-Based Node Similarities	Dec 1, 2018	Anomaly DetectionClustering	CodeCode Available	5
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI	Nov 28, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Adversarial Distillation of Bayesian Neural Network Posteriors	Jun 27, 2018	Active LearningAnomaly Detection	CodeCode Available	5
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection	May 14, 2021	Anomaly Detectionvalid	CodeCode Available	5
Improved Anomaly Detection by Using the Attention-Based Isolation Forest	Oct 5, 2022	Anomaly Detection	CodeCode Available	5
Associative Knowledge Graphs for Efficient Sequence Storage and Retrieval	Nov 19, 2024	Anomaly DetectionKnowledge Graphs	CodeCode Available	5
Anomalous Motion Detection on Highway Using Deep Learning	Jun 15, 2020	Anomaly DetectionDeep Learning	CodeCode Available	5
Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection	Oct 10, 2023	Anomaly Detection	CodeCode Available	5
Implementing Lightweight Intrusion Detection System on Resource Constrained Devices	Oct 28, 2024	Anomaly DetectionIntrusion Detection	CodeCode Available	5
Improved Anomaly Detection through Conditional Latent Space VAE Ensembles	Oct 16, 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	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