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 10761100 of 4856 papers

TitleStatusHype
Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly DetectionCode0
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex OptimizationCode0
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly DetectionCode0
Improving Vision Anomaly Detection with the Guidance of Language ModalityCode0
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and LocalizationCode0
Improving SIEM for Critical SCADA Water Infrastructures Using Machine LearningCode0
Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial NetworksCode0
A Showcase of the Use of Autoencoders in Feature Learning ApplicationsCode0
Improved Anomaly Detection through Conditional Latent Space VAE EnsemblesCode0
Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series MeasurementsCode0
Improved AutoEncoder with LSTM module and KL divergenceCode0
Improving Novelty Detection using the Reconstructions of Nearest NeighboursCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
Importance Weighted Adversarial Discriminative Transfer for Anomaly DetectionCode0
Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional AutoencodersCode0
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL DatasetCode0
Online Detection of Anomalies in Temporal Knowledge Graphs with InterpretabilityCode0
Image anomaly detection with capsule networks and imbalanced datasetsCode0
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature LearningCode0
Improved Anomaly Detection by Using the Attention-Based Isolation ForestCode0
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained FeatureCode0
Attribute Restoration Framework for Anomaly DetectionCode0
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse RepresentationsCode0
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based ApproachCode0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
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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