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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1025 of 4856 papers

TitleStatusHype
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things—0
Cellwise and Casewise Robust Covariance in High Dimensions—0
Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection—0
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect—0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation—0
Rethinking Metrics and Benchmarks of Video Anomaly Detection—0
eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems—0
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly DetectionCode0
Anomaly detection in radio galaxy data with trainable COSFIRE filtersCode0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images—0
Learning Normal Patterns in Musical Loops—0
MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network—0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge—0
Unsupervised Network Anomaly Detection with Autoencoders and Traffic ImagesCode0
A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams—0
PromptTAD: Object-Prompt Enhanced Traffic Anomaly DetectionCode0
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions—0
Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection—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
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
Unsupervised anomaly detection in MeV ultrafast electron diffraction—0
TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis—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