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 1026–1050 of 4856 papers

TitleStatusHype
Unsupervised Anomaly Detection Using Diffusion Trend Analysis—0
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and LocalizationCode3
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation—0
Real-Time Anomaly Detection and Reactive Planning with Large Language Models—0
Federated PCA on Grassmann Manifold for IoT Anomaly DetectionCode1
Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives—0
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection—0
Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing MonitoringCode0
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly DetectionCode0
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series DataCode0
Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance—0
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision—0
neuralGAM: Explainable generalized additive neural networks with independent neural network trainingCode0
ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access NetworksCode1
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection SimplifiedCode0
Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks—0
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning—0
CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks—0
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection—0
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method—0
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly DetectionCode0
Support Vector Based Anomaly Detection in Federated Learning—0
Looking for Tiny Defects via Forward-Backward Feature Transfer—0
Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time SequencesCode0
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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