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 326350 of 4856 papers

TitleStatusHype
Class Label-aware Graph Anomaly DetectionCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New BaselineCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisCode1
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
Coniferest: a complete active anomaly detection frameworkCode1
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
Correlation-aware Deep Generative Model for Unsupervised Anomaly DetectionCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal DistillationCode1
Can LLMs Understand Time Series Anomalies?Code1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
Dynamic Addition of Noise in a Diffusion Model for Anomaly DetectionCode1
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and LocalizationCode1
DATE: Detecting Anomalies in Text via Self-Supervision of TransformersCode1
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security ApplicationsCode1
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
4UniNetDetection AUROC99.9Unverified
5GLASSDetection AUROC99.9Unverified
6PBASDetection AUROC99.8Unverified
7HETMMDetection AUROC99.8Unverified
8INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10EfficientAD (early stopping)Detection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
5INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
6DDADDetection AUROC98.9Unverified
7GLASSDetection AUROC98.8Unverified
8DiffusionADDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified