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 376400 of 4856 papers

TitleStatusHype
TRANSOM: An Efficient Fault-Tolerant System for Training LLMsCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
PAD: A Dataset and Benchmark for Pose-agnostic Anomaly DetectionCode1
Learning a Cross-modality Anomaly Detector for Remote Sensing ImageryCode1
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series PretrainingCode1
A Discrepancy Aware Framework for Robust Anomaly DetectionCode1
IPMix: Label-Preserving Data Augmentation Method for Training Robust ClassifiersCode1
Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice SystemCode1
T-Rep: Representation Learning for Time Series using Time-EmbeddingsCode1
Delving into CLIP latent space for Video Anomaly RecognitionCode1
Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly DetectionCode1
ADGym: Design Choices for Deep Anomaly DetectionCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
LogGPT: Log Anomaly Detection via GPTCode1
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series ImagingCode1
Real3D-AD: A Dataset of Point Cloud Anomaly DetectionCode1
On Data Fabrication in Collaborative Vehicular Perception: Attacks and CountermeasuresCode1
Two-stage coarse-to-fine image anomaly segmentation and detection modelCode1
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly DetectionCode1
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive LearningCode1
GLAD: Content-aware Dynamic Graphs For Log Anomaly DetectionCode1
TSGBench: Time Series Generation BenchmarkCode1
A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection TechniquesCode1
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain TestingCode1
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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