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

Showing 951–1000 of 4856 papers

TitleStatusHype
Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications—0
Trustworthy Prediction with Gaussian Process Knowledge ScoresCode0
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017—0
Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems—0
Searching for a Hidden Markov Anomaly over Multiple Processes—0
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments—0
Signatures to help interpretability of anomalies—0
Evaluation Pipeline for systematically searching for Anomaly Detection Systems—0
Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders—0
Latent Anomaly Detection: Masked VQ-GAN for Unsupervised Segmentation in Medical CBCT—0
Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly DetectionCode0
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
Polyra Swarms: A Shape-Based Approach to Machine Learning—0
Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss—0
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles—0
Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMsCode0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation—0
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical DomainCode0
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information—0
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization—0
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications—0
HomographyAD: Deep Anomaly Detection Using Self Homography Learning—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
MAMBO: High-Resolution Generative Approach for Mammography Images—0
Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measures—0
MLOps with Microservices: A Case Study on the Maritime Domain—0
Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs—0
Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline—0
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems—0
An AI-Based Public Health Data Monitoring System—0
System Calls for Malware Detection and Classification: Methodologies and Applications—0
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric RepresentationCode0
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded DevicesCode0
Generator Based Inference (GBI)Code0
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection—0
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCCode0
SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems—0
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach—0
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms—0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning—0
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation—0
Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning—0
Is Hyperbolic Space All You Need for Medical Anomaly Detection?—0
Fog Intelligence for Network Anomaly Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4UniNetDetection AUROC99.9—Unverified
5GLASSDetection AUROC99.9—Unverified
6PBASDetection AUROC99.8—Unverified
7HETMMDetection AUROC99.8—Unverified
8INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10EfficientAD (early stopping)Detection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
5INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
6DDADDetection AUROC98.9—Unverified
7GLASSDetection AUROC98.8—Unverified
8DiffusionADDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified