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 1801–1825 of 4856 papers

TitleStatusHype
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision—0
neuralGAM: Explainable generalized additive neural networks with independent neural network trainingCode0
Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks—0
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning—0
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection SimplifiedCode0
CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks—0
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection—0
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method—0
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly DetectionCode0
Looking for Tiny Defects via Forward-Backward Feature Transfer—0
An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw CycleCode0
Support Vector Based Anomaly Detection in Federated Learning—0
Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time SequencesCode0
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization—0
Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial FlowsCode0
Domain-independent detection of known anomaliesCode0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining TasksCode0
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning—0
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly DetectionCode0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly DetectionCode0
Real-Time Energy Measurement for Non-Intrusive Well-Being Monitoring of Elderly People -- a Case Study—0
Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection—0
ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural BehaviorCode0
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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
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (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