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

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Showing 3051–3100 of 4856 papers

TitleStatusHype
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing—0
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly DetectionCode1
Online false discovery rate control for anomaly detection in time series—0
Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder—0
Autoencoders for Semivisible Jet Detection—0
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 Segmentation—0
Auditing Keyword Queries Over Text Documents—0
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
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 Matching—0
A novel data-driven algorithm to predict anomalous prescription based on patient's feature set—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
Anomaly Rule Detection in Sequence Data—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
SQUID: Deep Feature In-Painting for Unsupervised Anomaly DetectionCode1
A Taxonomy of Anomalies in Log Data—0
SLA^2P: Self-supervised Anomaly Detection with Adversarial PerturbationCode1
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Deep Representation Learning with an Information-theoretic Loss—0
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes—0
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's DistanceCode0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving ScenesCode1
Link Analysis meets Ontologies: Are Embeddings the Answer?Code0
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection—0
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality DetectionCode0
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection ScoringCode0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model—0
Self-Supervised Predictive Convolutional Attentive Block for Anomaly DetectionCode1
Online Self-Evolving Anomaly Detection in Cloud Computing EnvironmentsCode0
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly DetectionCode1
Learning Graph Neural Networks for Multivariate Time Series Anomaly DetectionCode1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing FlowsCode1
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
Variation and generality in encoding of syntactic anomaly information in sentence embeddings—0
Through-Foliage Tracking with Airborne Optical Sectioning—0
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts—0
Improving Novelty Detection using the Reconstructions of Nearest NeighboursCode0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Online-compatible Unsupervised Non-resonant Anomaly DetectionCode0
Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security—0
A Novel Data Pre-processing Technique: Making Data Mining Robust to Different Units and Scales of Measurement—0
A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified