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 4591–4600 of 4856 papers

Title	Date	Tasks	Status	Hype
ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications	Jan 22, 2018	Anomaly DetectionClassification	—Unverified	0
WEAC: Word embeddings for anomaly classification from event logs	Jan 18, 2018	Anomaly ClassificationAnomaly Detection	—Unverified	0
Learning Deep Features for One-Class Classification	Jan 16, 2018	Anomaly DetectionDescriptive	CodeCode Available	0
Real-world Anomaly Detection in Surveillance Videos	Jan 12, 2018	Abnormal Event Detection In VideoActivity Recognition	CodeCode Available	1
Detecting abnormal events in video using Narrowed Normality Clusters	Jan 12, 2018	Anomaly DetectionClustering	—Unverified	0
Paranom: A Parallel Anomaly Dataset Generator	Jan 9, 2018	Anomaly DetectionGeneral Classification	—Unverified	0
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos	Jan 9, 2018	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection	Jan 9, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Precision and Recall for Range-Based Anomaly Detection	Jan 9, 2018	Anomaly Detection	—Unverified	0
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines	Jan 1, 2018	Anomaly DetectionBIG-bench Machine 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