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 3731–3740 of 4856 papers

Title	Date	Tasks	Status
Anomaly detection with superexperts under delayed feedback	Oct 8, 2020	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Anomaly Detection in Large Labeled Multi-Graph Databases	Oct 7, 2020	Anomaly Detection	—Unverified
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect	Oct 7, 2020	Anomaly DetectionDeep Learning	—Unverified
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data	Oct 6, 2020	Anomaly Detection	—Unverified
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray	Oct 6, 2020	Anomaly DetectionCOVID-19 Diagnosis	CodeCode Available
Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays	Oct 6, 2020	Anomaly Detection	CodeCode Available
OneFlow: One-class flow for anomaly detection based on a minimal volume region	Oct 6, 2020	Anomaly DetectionOne-class classifier	—Unverified
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining	Oct 6, 2020	Anomaly DetectionDenoising	—Unverified
Global soil moisture from in-situ measurements using machine learning -- SoMo.ml	Oct 5, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Deep Anomaly Detection by Residual Adaptation	Oct 5, 2020	Anomaly DetectionDisentanglement	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	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