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 33013325 of 4856 papers

TitleStatusHype
A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network0
Approximate Maximum Halfspace Discrepancy0
Task-agnostic Continual Learning with Hybrid Probabilistic Models0
Generalized One-Class Learning Using Pairs of Complementary Classifiers0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection0
A new Video Synopsis Based Approach Using Stereo Camera0
Detecting Anomalous User Behavior in Remote Patient Monitoring0
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression0
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis0
Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time SeriesCode0
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density EstimationCode0
Large-Scale Network Embedding in Apache Spark0
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison0
Deep Learning in Latent Space for Video Prediction and CompressionCode1
TS2Vec: Towards Universal Representation of Time SeriesCode1
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
Anomaly Detection in Dynamic Graphs via TransformerCode1
Anomaly Detection and Automated Labeling for Voter Registration File Changes0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions0
Anomaly Detection in Video Sequences: A Benchmark and Computational ModelCode1
X-MAN: Explaining multiple sources of anomalies in video0
FastAno: Fast Anomaly Detection via Spatio-temporal Patch TransformationCode1
Towards Total Recall in Industrial Anomaly DetectionCode2
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional 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