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 45264550 of 4856 papers

TitleStatusHype
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving0
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data StreamsCode0
Future Frame Prediction for Anomaly Detection – A New BaselineCode1
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems0
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsCode0
Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder0
Deep Anomaly Detection Using Geometric TransformationsCode1
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning0
AVID: Adversarial Visual Irregularity DetectionCode0
Deep Active Learning for Anomaly Detection0
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
A Compact Convolutional Neural Network for Textured Surface Anomaly DetectionCode0
Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep AutoencodersCode0
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic0
Incorporating Privileged Information to Unsupervised Anomaly Detection0
Population Anomaly Detection through Deep Gaussianization0
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.0
Dysarthric speech evaluation: automatic and perceptual approaches0
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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