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 48264850 of 4856 papers

TitleStatusHype
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset0
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation0
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations0
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection0
Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data0
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders0
In-body Bionanosensor Localization for Anomaly Detection via Inertial Positioning and THz Backscattering Communication0
Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles0
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images0
Incident Detection on Junctions Using Image Processing0
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability0
Incomplete Pivoted QR-based Dimensionality Reduction0
Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series0
Incorporating Privileged Information to Unsupervised Anomaly Detection0
InDeed: Interpretable image deep decomposition with guaranteed generalizability0
Inductive Representation Learning in Large Attributed Graphs0
Inferring Power Grid Information with Power Line Communications: Review and Insights0
Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillation0
Feature Clustering for Support Identification in Extreme Regions0
Infrared: A Meta Bug Detector0
Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection0
Insightful Assistant: AI-compatible Operation Graph Representations for Enhancing Industrial Conversational Agents0
In-situ Anomaly Detection in Additive Manufacturing with Graph Neural Networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified