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 101–125 of 4856 papers

TitleStatusHype
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge—0
MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network—0
A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams—0
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images—0
PromptTAD: Object-Prompt Enhanced Traffic Anomaly DetectionCode0
Unsupervised Network Anomaly Detection with Autoencoders and Traffic ImagesCode0
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions—0
Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection—0
Unified AI for Accurate Audio Anomaly Detection—0
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models—0
Anomaly Detection Based on Critical Paths for Deep Neural Networks—0
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph CoarseningCode0
Unsupervised anomaly detection in MeV ultrafast electron diffraction—0
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
Structure-based Anomaly Detection and Clustering—0
TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis—0
Just Dance with π! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection—0
PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving ExperimentsCode0
Are vision language models robust to uncertain inputs?—0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection—0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection—0
Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series—0
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing—0
Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network—0
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review—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