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 851875 of 4856 papers

TitleStatusHype
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly DetectionCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge GraphsCode1
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly DetectionCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Self-Supervised Masked Convolutional Transformer Block for Anomaly DetectionCode1
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and LocalizationCode1
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly DetectionCode1
Self-Supervised Video Forensics by Audio-Visual Anomaly DetectionCode1
Semi-supervised Anomaly Detection using AutoEncodersCode1
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial LearningCode1
SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical ImagingCode1
A Survey of Visual Sensory Anomaly DetectionCode1
A Survey of World Models for Autonomous DrivingCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
BIVA: A Very Deep Hierarchy of Latent Variables for Generative ModelingCode1
BMAD: Benchmarks for Medical Anomaly DetectionCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
Beyond Individual Input for Deep Anomaly Detection on Tabular DataCode1
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge DistillationCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
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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