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 2001–2050 of 4856 papers

TitleStatusHype
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Active Learning for Network Intrusion Detection—0
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection—0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection—0
EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models—0
Attention Guided Anomaly Localization in Images—0
e-G2C: A 0.14-to-8.31 μJ/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM—0
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume—0
Efficient Representation of the Activation Space in Deep Neural Networks—0
Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes—0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment—0
Exploring time-series motifs through DTW-SOM—0
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?—0
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection—0
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
Efficient pattern-based anomaly detection in a network of multivariate devices—0
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks—0
Anomaly Detection with Neural Parsers That Never Reject—0
Extracting Information from Indian First Names—0
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs—0
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
Anomaly detection and regime searching in fitness-tracker data—0
Efficient Nonlinear RX Anomaly Detectors—0
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection—0
Attention Boosted Autoencoder for Building Energy Anomaly Detection—0
Efficiently Discovering Frequent Motifs in Large-scale Sensor Data—0
Attention-Based Self-Supervised Feature Learning for Security Data—0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks—0
Anomaly detection and motif discovery in symbolic representations of time series—0
Fair Anomaly Detection For Imbalanced Groups—0
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution—0
Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer—0
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection—0
Anomaly Detection and Modeling in 802.11 Wireless Networks—0
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems—0
Efficient Client Selection in Federated Learning—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning—0
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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