SOTAVerified

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

Showing 34263450 of 4856 papers

TitleStatusHype
What is Wrong with One-Class Anomaly Detection?Code1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series0
Autoencoders for unsupervised anomaly detection in high energy physics0
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands0
Hop-Count Based Self-Supervised Anomaly Detection on Attributed NetworksCode0
Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector0
OneLog: Towards End-to-End Training in Software Log Anomaly Detection0
An ADMM-based Optimal Transmission Frequency Management System for IoT Edge Intelligence0
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative LearningCode1
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees0
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance0
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
Global Information Guided Video Anomaly Detection0
ADNet: Temporal Anomaly Detection in Surveillance VideosCode1
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression0
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
Mixed supervision for surface-defect detection: from weakly to fully supervised learningCode1
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance0
Efficient Model Monitoring for Quality Control in Cardiac Image SegmentationCode0
Using a Neural Network to Detect Anomalies given an N-gram Profile0
LearningCity: Knowledge Generation for Smart Cities0
DATE: Detecting Anomalies in Text via Self-Supervision of TransformersCode1
A Principled Approach to Enriching Security-related Data for Running Processes through Statistics and Natural Language ProcessingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified