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 32513275 of 4856 papers

TitleStatusHype
Margin-Aware Intra-Class Novelty Identification for Medical ImagesCode0
Anomaly Detection with Neural Parsers That Never Reject0
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs0
Using Visual Anomaly Detection for Task Execution MonitoringCode0
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection0
Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals0
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders0
Discriminative-Generative Representation Learning for One-Class Anomaly Detection0
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation0
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection0
Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments0
HURRA! Human readable router anomaly detection0
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene SegmentationCode1
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary0
Anomaly Detection via Self-organizing MapCode0
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in ImagesCode0
Image-Hashing-Based Anomaly Detection for Privacy-Preserving Online Proctoring0
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection0
OnlineSTL: Scaling Time Series Decomposition by 100x0
Explainable Debugger for Black-box Machine Learning ModelsCode0
PICASO: Permutation-Invariant Cascaded Attentional Set OperatorCode0
Time Series Anomaly Detection for Smart Grids: A Survey0
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (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