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 4381–4390 of 4856 papers

Title	Date	Tasks	Status	Hype
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection	Feb 1, 2019	Anomaly Detection	CodeCode Available	0
Unsupervised Prediction of Negative Health Events Ahead of Time	Jan 31, 2019	Anomaly DetectionClustering	—Unverified	0
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks	Jan 30, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available	0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning	Jan 30, 2019	Anomaly DetectionEnsemble Learning	—Unverified	0
Anomaly Locality in Video Surveillance	Jan 29, 2019	Anomaly Detection	—Unverified	0
Heartbeat Anomaly Detection using Adversarial Oversampling	Jan 28, 2019	Anomaly DetectionDiagnostic	CodeCode Available	0
Generalization of feature embeddings transferred from different video anomaly detection domains	Jan 28, 2019	Anomaly DetectionDomain Generalization	—Unverified	0
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection	Jan 25, 2019	Anomaly DetectionDecoder	CodeCode Available	0
One-Class Convolutional Neural Network	Jan 24, 2019	Anomaly DetectionGeneral Classification	CodeCode Available	1
Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey	Jan 24, 2019	Anomaly Detection	—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