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 37263750 of 4856 papers

TitleStatusHype
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm RateCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation0
Algorithmic Frameworks for the Detection of High Density AnomaliesCode0
Unsupervised 3D Brain Anomaly Detection0
Anomaly detection with superexperts under delayed feedbackCode0
Anomaly Detection in Large Labeled Multi-Graph Databases0
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect0
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data0
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-rayCode0
Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-raysCode0
OneFlow: One-class flow for anomaly detection based on a minimal volume region0
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining0
Global soil moisture from in-situ measurements using machine learning -- SoMo.ml0
Deep Anomaly Detection by Residual Adaptation0
Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting0
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series0
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed NetworksCode0
Driver Anomaly Detection: A Dataset and Contrastive Learning ApproachCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
A comparison of classical and variational autoencoders for anomaly detection0
A Survey on Deep Learning Techniques for Video Anomaly Detection0
Anomaly Detection and Sampling Cost Control via Hierarchical GANs0
STAN: Synthetic Network Traffic Generation with Generative Neural ModelsCode0
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization0
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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