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 551575 of 4856 papers

TitleStatusHype
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Tree Detection and Diameter Estimation Based on Deep LearningCode1
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detectionCode1
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical EncodingsCode1
Self-supervised Sparse Representation for Video Anomaly DetectionCode1
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly DetectionCode1
Anomaly Detection Requires Better RepresentationsCode1
DAGAD: Data Augmentation for Graph Anomaly DetectionCode1
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency AnalysisCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
Asymmetric Student-Teacher Networks for Industrial Anomaly DetectionCode1
Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentCode1
Overlooked Video Classification in Weakly Supervised Video Anomaly DetectionCode1
Towards Continual Adaptation in Industrial Anomaly DetectionCode1
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and LocalizationCode1
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical imagesCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine LearningCode1
Self-Supervised Masked Convolutional Transformer Block for Anomaly DetectionCode1
Anomaly Detection in Aerial Videos with TransformersCode1
Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection ModelCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Improving Generalizability of Graph Anomaly Detection Models via Data AugmentationCode1
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile RobotsCode1
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (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