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 1001–1050 of 4856 papers

TitleStatusHype
Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection—0
Cellwise and Casewise Robust Covariance in High Dimensions—0
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things—0
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect—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
Rethinking Metrics and Benchmarks of Video Anomaly Detection—0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation—0
Anomaly detection in radio galaxy data with trainable COSFIRE filtersCode0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge—0
Learning Normal Patterns in Musical Loops—0
Unsupervised Network Anomaly Detection with Autoencoders and Traffic ImagesCode0
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images—0
A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams—0
MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network—0
PromptTAD: Object-Prompt Enhanced Traffic Anomaly DetectionCode0
Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection—0
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions—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
Structure-based Anomaly Detection and Clustering—0
TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis—0
Unsupervised anomaly detection in MeV ultrafast electron diffraction—0
Just Dance with π! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection—0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection—0
Are vision language models robust to uncertain inputs?—0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection—0
PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving ExperimentsCode0
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
Fairness-aware Anomaly Detection via Fair Projection—0
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark—0
Preference Isolation Forest for Structure-based Anomaly Detection—0
Hashing for Structure-based Anomaly DetectionCode0
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
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
PIF: Anomaly detection via preference embedding—0
A Representation Learning Approach to Feature Drift Detection in Wireless Networks—0
Cybersecurity threat detection based on a UEBA framework using Deep Autoencoders—0
WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes—0
Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks—0
Structural-Temporal Coupling Anomaly Detection with Dynamic Graph TransformerCode0
Crowd Scene Analysis using Deep Learning Techniques—0
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder—0
Isolation Forest in Novelty Detection Scenario—0
neuralGAM: An R Package for Fitting Generalized Additive Neural Networks—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