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

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Showing 2001–2025 of 4856 papers

TitleStatusHype
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection—0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection—0
Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume—0
Efficient Representation of the Activation Space in Deep Neural Networks—0
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection—0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
Efficient pattern-based anomaly detection in a network of multivariate devices—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
Anomaly detection and regime searching in fitness-tracker data—0
Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes—0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment—0
Exploring time-series motifs through DTW-SOM—0
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?—0
Efficient Nonlinear RX Anomaly Detectors—0
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection—0
Attention Boosted Autoencoder for Building Energy Anomaly Detection—0
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks—0
Efficiently Discovering Frequent Motifs in Large-scale Sensor Data—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified