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 30513075 of 4856 papers

TitleStatusHype
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing0
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly DetectionCode1
Online false discovery rate control for anomaly detection in time series0
Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder0
Autoencoders for Semivisible Jet Detection0
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Constrained Adaptive Projection with Pretrained Features for Anomaly DetectionCode0
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objectsCode0
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation0
Auditing Keyword Queries Over Text Documents0
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision0
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge DistillationCode1
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenariosCode0
Reconstruction Student with Attention for Student-Teacher Pyramid Matching0
A novel data-driven algorithm to predict anomalous prescription based on patient's feature set0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data0
Anomaly Rule Detection in Sequence Data0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection0
SQUID: Deep Feature In-Painting for Unsupervised Anomaly DetectionCode1
A Taxonomy of Anomalies in Log Data0
SLA^2P: Self-supervised Anomaly Detection with Adversarial PerturbationCode1
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection0
Deep Representation Learning with an Information-theoretic Loss0
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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