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 25512575 of 4856 papers

TitleStatusHype
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics0
Self-supervised Learning for Clustering of Wireless Spectrum ActivityCode0
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural NetworksCode0
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping0
Oracle Analysis of Representations for Deep Open Set Detection0
Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data0
Contrastive Learning for Time Series on Dynamic Graphs0
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes0
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics0
Improving Generalizability of Graph Anomaly Detection Models via Data AugmentationCode1
Learning Acceptance Regions for Many Classes with Anomaly Detection0
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile RobotsCode1
Collaborative Anomaly Detection0
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model0
Infrared: A Meta Bug Detector0
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks0
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving VideosCode1
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning0
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning0
Analytics and Machine Learning Powered Wireless Network Optimization and Planning0
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels0
TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly DetectionCode0
Real-world Video Anomaly Detection by Extracting Salient Features in Videos0
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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