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 351375 of 4856 papers

TitleStatusHype
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
AnoDFDNet: A Deep Feature Difference Network for Anomaly DetectionCode1
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest RadiographsCode1
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-raysCode1
ADGym: Design Choices for Deep Anomaly DetectionCode1
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and LocalizationCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving VideosCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Deep SetsCode1
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicCode1
A Discrepancy Aware Framework for Robust Anomaly DetectionCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Delving into CLIP latent space for Video Anomaly RecognitionCode1
Demystifying and Extracting Fault-indicating Information from Logs for Failure DiagnosisCode1
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly DetectorCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
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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