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

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Showing 2976–3000 of 4856 papers

TitleStatusHype
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational AutoencoderCode1
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
Bioinspired Cortex-based Fast Codebook Generation—0
Time-Series Anomaly Detection with Implicit Neural RepresentationCode1
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding—0
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms—0
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
Community-based anomaly detection using spectral graph filtering—0
COVID-19 Detection Using CT Image Based On YOLOv5 Network—0
Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices—0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series—0
Compressed Smooth Sparse Decomposition—0
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series DataCode2
Online Time Series Anomaly Detection with State Space Gaussian Processes—0
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging—0
Real-Time Anomaly Detection for Multivariate Data Stream—0
FCDD - Explainable Anomaly Detection—0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)—0
Concise Logarithmic Loss Function for Robust Training of Anomaly Detection Model—0
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection—0
Functional Anomaly Detection: a Benchmark Study—0
Local2Global: A distributed approach for scaling representation learning on graphsCode0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified