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 4111–4120 of 4856 papers

Title	Date	Tasks	Status
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection	Nov 27, 2019	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data	Nov 25, 2019	Anomaly DetectionIntrusion Detection	—Unverified
Attribute Restoration Framework for Anomaly Detection	Nov 25, 2019	Anomaly DetectionAttribute	CodeCode Available
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces	Nov 24, 2019	Anomaly DetectionSupervised Anomaly Detection	—Unverified
Latent space conditioning for improved classification and anomaly detection	Nov 24, 2019	Anomaly DetectionClassification	—Unverified
EvAn: Neuromorphic Event-based Anomaly Detection	Nov 21, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM	Nov 21, 2019	Anomaly DetectionDiversity	CodeCode Available
Generalizing Information to the Evolution of Rational Belief	Nov 21, 2019	Anomaly Detectionfeature selection	—Unverified
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU	Nov 20, 2019	Anomaly DetectionClassification	—Unverified
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks	Nov 20, 2019	Anomaly DetectionBIG-bench Machine Learning	—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