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 1101–1125 of 4856 papers

TitleStatusHype
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
Anomaly detection and regime searching in fitness-tracker data—0
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Attention Boosted Autoencoder for Building Energy Anomaly Detection—0
Attention-Based Self-Supervised Feature Learning for Security Data—0
Anomaly detection and motif discovery in symbolic representations of time series—0
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection—0
Anomaly Detection and Modeling in 802.11 Wireless Networks—0
Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network—0
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model—0
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning—0
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning—0
Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching—0
AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System—0
Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection—0
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models—0
Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection—0
Attack-Agnostic Adversarial Detection—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Anomaly Detection and Localisation using Mixed Graphical Models—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