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 1976–2000 of 4856 papers

TitleStatusHype
Explainable Transformer-Based Anomaly Detection for Internet of Things Security Check for updates—0
Explainable Unsupervised Anomaly Detection with Random Forest—0
Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?—0
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
Explaining Anomalies with Tensor Networks—0
Crowd Scene Analysis using Deep Learning Techniques—0
Crowd-level Abnormal Behavior Detection via Multi-scale Motion Consistency Learning—0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
Crowded Scene Analysis: A Survey—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT—0
Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection—0
Exploring Diffusion Models for Unsupervised Video Anomaly Detection—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security—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