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 44264450 of 4856 papers

TitleStatusHype
WAIC, but Why? Generative Ensembles for Robust Anomaly DetectionCode0
SaLite : A light-weight model for salient object detectionCode0
Deep Semi-Supervised Anomaly DetectionCode0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep AutoencodersCode0
SAM-kNN Regressor for Online Learning in Water Distribution NetworksCode0
Generator Based Inference (GBI)Code0
Uncertainty on Asynchronous Time Event PredictionCode0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTMCode0
Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder TrainingCode0
BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly DetectionCode0
Generative Neural Networks for Anomaly Detection in Crowded ScenesCode0
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly DetectionCode0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featuresCode0
Network Traffic Anomaly Detection Using Recurrent Neural NetworksCode0
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth MeasureCode0
Scalable Motif Counting for Large-scale Temporal GraphsCode0
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing PipelinesCode0
Neural Collaborative Filtering to Detect Anomalies in Human Semantic TrajectoriesCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron ColliderCode0
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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