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 4041–4050 of 4856 papers

Title	Date	Tasks	Status	Hype
Iterative energy-based projection on a normal data manifold for anomaly localization	Feb 10, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models	Feb 9, 2020	Anomaly DetectionGroup Anomaly Detection	—Unverified	0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data	Feb 7, 2020	Anomaly DetectionTime Series	—Unverified	0
Memory Augmented Generative Adversarial Networks for Anomaly Detection	Feb 7, 2020	Anomaly Detection	—Unverified	0
movie2trailer: Unsupervised trailer generation using Anomaly detection	Feb 5, 2020	Anomaly Detection	—Unverified	0
Anomaly Detection by One Class Latent Regularized Networks	Feb 5, 2020	Anomaly Detection	—Unverified	0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos	Feb 4, 2020	Action DetectionAction Recognition	—Unverified	0
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks	Feb 4, 2020	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Unsupervised Anomaly Detection for X-Ray Images	Jan 29, 2020	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Ensemble Grammar Induction For Detecting Anomalies in Time Series	Jan 29, 2020	Anomaly DetectionEnsemble Learning	—Unverified	0

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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