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 3251–3275 of 4856 papers

TitleStatusHype
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting—0
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
Robust Anomaly Detection for Time-series Data—0
Robust Audio Anomaly Detection—0
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
Bioinspired Cortex-based Fast Codebook Generation—0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding—0
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms—0
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
Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices—0
COVID-19 Detection Using CT Image Based On YOLOv5 Network—0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series—0
Compressed Smooth Sparse Decomposition—0
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging—0
Online Time Series Anomaly Detection with State Space Gaussian Processes—0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
Real-Time Anomaly Detection for Multivariate Data Stream—0
FCDD - Explainable Anomaly Detection—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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