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 1301–1350 of 4856 papers

TitleStatusHype
Position: Untrained Machine Learning for Anomaly Detection—0
Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making—0
TopoCL: Topological Contrastive Learning for Time Series—0
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
LAST SToP For Modeling Asynchronous Time Series—0
Anomaly Detection via Autoencoder Composite Features and NCE—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
A Poisson Process AutoDecoder for X-ray Sources—0
ConditionNET: Learning Preconditions and Effects for Execution Monitoring—0
Using Causality for Enhanced Prediction of Web Traffic Time Series—0
Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework—0
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes—0
An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus—0
DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics ExperimentsCode0
Battery State of Health Estimation Using LLM Framework—0
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units—0
Detecting Anomalies Using Rotated Isolation Forest—0
si4onnx: A Python package for Selective Inference in Deep Learning Models—0
MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction—0
Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis—0
LLM Assisted Anomaly Detection Service for Site Reliability Engineers: Enhancing Cloud Infrastructure Resilience—0
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems—0
Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect CommunicationCode0
Large Models in Dialogue for Active Perception and Anomaly DetectionCode0
Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL ChallengeCode0
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
Mitigating Spurious Negative Pairs for Robust Industrial Anomaly DetectionCode0
Kernel-Based Anomaly Detection Using Generalized Hyperbolic ProcessesCode0
Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector—0
Median of Forests for Robust Density Estimation—0
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs—0
Efficient Client Selection in Federated Learning—0
Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case—0
"Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection—0
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models—0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly DetectionCode0
Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity—0
Autoencoders for Anomaly Detection are Unreliable—0
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality—0
Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid—0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach—0
TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection—0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection—0
Score Combining for Contrastive OOD Detection—0
Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection—0
Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: 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