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 45764600 of 4856 papers

TitleStatusHype
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path SignatureCode0
Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection SystemCode0
Online Self-Evolving Anomaly Detection in Cloud Computing EnvironmentsCode0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural NetworksCode0
Online Topology Identification from Vector Autoregressive Time SeriesCode0
DAD++: Improved Data-free Test Time Adversarial DefenseCode0
Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysisCode0
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
On Periodicity Detection and Structural Periodic SimilarityCode0
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-raysCode0
Unsupervised real-time anomaly detection for streaming dataCode0
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly DetectionCode0
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial NetworksCode0
on the effectiveness of generative adversarial network on anomaly detectionCode0
FADE: Forecasting for Anomaly Detection on ECGCode0
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral imagesCode0
Semantic Novelty Detection in Natural Language DescriptionsCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical TransientsCode0
Semi-automatic staging area for high-quality structured data extraction from scientific literatureCode0
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent EmbeddingsCode0
DABL: Detecting Semantic Anomalies in Business Processes Using Large Language ModelsCode0
Backdoor Attack against One-Class Sequential Anomaly Detection ModelsCode0
(1 + )-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data SetsCode0
On the True Distribution Approximation of Minimum Bayes-Risk DecodingCode0
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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