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 14261450 of 4856 papers

TitleStatusHype
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical ReasoningCode0
Fence GAN: Towards Better Anomaly DetectionCode0
CNTS: Cooperative Network for Time SeriesCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Cluster-Wide Task Slowdown Detection in Cloud SystemCode0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
Machine learning-based identification of Gaia astrometric exoplanet orbitsCode0
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal SamplesCode0
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly DetectionCode0
FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing TasksCode0
Fast Particle-based Anomaly Detection Algorithm with Variational AutoencoderCode0
Feature space reduction as data preprocessing for the anomaly detectionCode0
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objectsCode0
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User AnomaliesCode0
CloudShield: Real-time Anomaly Detection in the CloudCode0
Fast Adaptive RNN Encoder-Decoder for Anomaly Detection in SMD Assembly MachineCode0
Achieving Counterfactual Fairness for Anomaly DetectionCode0
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIPCode0
Margin-Aware Intra-Class Novelty Identification for Medical ImagesCode0
Anomaly Detection in Video Sequence with Appearance-Motion CorrespondenceCode0
FADE: Forecasting for Anomaly Detection on ECGCode0
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesCode0
FABLE : Fabric Anomaly Detection Automation ProcessCode0
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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