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 4341–4350 of 4856 papers

Title	Date	Tasks	Status
Online Topology Identification from Vector Autoregressive Time Series	Apr 3, 2019	Anomaly DetectionTime Series	CodeCode Available
Using Google Analytics to Support Cybersecurity Forensics	Apr 3, 2019	Anomaly DetectionMarketing	—Unverified
Fence GAN: Towards Better Anomaly Detection	Apr 2, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier	Apr 2, 2019	Anomaly DetectionDimensionality Reduction	—Unverified
Active Learning for Network Intrusion Detection	Apr 2, 2019	Active LearningAnomaly Detection	—Unverified
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models	Apr 1, 2019	Anomaly DetectionContextual Anomaly Detection	—Unverified
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection	Mar 30, 2019	Anomaly DetectionDecoder	CodeCode Available
Autoencoding Binary Classifiers for Supervised Anomaly Detection	Mar 26, 2019	Anomaly DetectionSupervised Anomaly Detection	—Unverified
Spatially-weighted Anomaly Detection with Regression Model	Mar 23, 2019	Anomaly Detectionmodel	—Unverified
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations	Mar 20, 2019	Anomaly DetectionDenoising	—Unverified

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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