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 3621–3630 of 4856 papers

Title	Date	Tasks	Status
OneLog: Towards End-to-End Training in Software Log Anomaly Detection	Apr 15, 2021	Anomaly Detection	—Unverified
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence	Apr 15, 2021	Anomaly DetectionManagement	—Unverified
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees	Apr 14, 2021	Anomaly Detection	—Unverified
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance	Apr 14, 2021	Anomaly DetectionContextual Anomaly Detection	—Unverified
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression	Apr 14, 2021	Anomaly Detectionregression	—Unverified
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks	Apr 14, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Global Information Guided Video Anomaly Detection	Apr 14, 2021	Anomaly DetectionSpatial Reasoning	—Unverified
Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning	Apr 13, 2021	Anomaly DetectionGaussian Processes	CodeCode Available
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance	Apr 13, 2021	Anomaly Detectionimage-classification	—Unverified
Using a Neural Network to Detect Anomalies given an N-gram Profile	Apr 12, 2021	Anomaly Detection	—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