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 2176–2200 of 4856 papers

TitleStatusHype
A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation—0
Domain Adaptation via Anaomaly Detection—0
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective—0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model—0
Does Your Phone Know Your Touch?—0
A Survey of Anomaly Detection in In-Vehicle Networks—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
A Survey of Anomaly Detection in Cyber-Physical Systems—0
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
DOC3-Deep One Class Classification using Contradictions—0
A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults—0
Do autoencoders need a bottleneck for anomaly detection?—0
A Subspace Projection Approach to Autoencoder-based Anomaly Detection—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems—0
A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive Testing—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy—0
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods—0
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection—0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
AstroM^3: A self-supervised multimodal model for astronomy—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