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 30013025 of 4856 papers

TitleStatusHype
Unsupervised textile defect detection using convolutional neural networks0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion0
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications0
Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders0
Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage0
Unsupervised Two-Stage Anomaly Detection0
Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism0
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features0
Unsupervised Visual Defect Detection with Score-Based Generative Model0
Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection0
Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities0
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning0
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection0
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality0
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks0
Use of in-the-wild images for anomaly detection in face anti-spoofing0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework0
Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence0
Using a Neural Network to Detect Anomalies given an N-gram Profile0
Using anomaly detection to support classification of fast running (packaging) processes0
Using Bursty Announcements for Detecting BGP Routing Anomalies0
Using Causality for Enhanced Prediction of Web Traffic Time Series0
Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach0
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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