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 351400 of 4856 papers

TitleStatusHype
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive LearningCode1
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case StudyCode1
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest RadiographsCode1
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-raysCode1
ADGym: Design Choices for Deep Anomaly DetectionCode1
Diversity-Measurable Anomaly DetectionCode1
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving VideosCode1
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low ToleranceCode1
Automating Outlier Detection via Meta-LearningCode1
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
A Discrepancy Aware Framework for Robust Anomaly DetectionCode1
Auto-Encoding Variational BayesCode1
Exploring Image Augmentations for Siamese Representation Learning with Chest X-RaysCode1
A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR DataCode1
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural NetworksCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Diffusion Models for Medical Anomaly DetectionCode1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
BatchNorm-based Weakly Supervised Video Anomaly DetectionCode1
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly DetectionCode1
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale LearningCode1
ADNet: Temporal Anomaly Detection in Surveillance VideosCode1
Fast Distance-based Anomaly Detection in Images Using an Inception-like AutoencoderCode1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing FlowsCode1
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly DetectionCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Beyond Individual Input for Deep Anomaly Detection on Tabular DataCode1
Diffusion Models with Implicit Guidance for Medical Anomaly DetectionCode1
Diffusion-Based Electrocardiography Noise Quantification via Anomaly DetectionCode1
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile RobotsCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
A Comprehensive Survey on Graph Anomaly Detection with Deep LearningCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial LearningCode1
Anomaly localization by modeling perceptual featuresCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry HousesCode1
FewSOME: One-Class Few Shot Anomaly Detection with Siamese NetworksCode1
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly DetectionCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
CableInspect-AD: An Expert-Annotated Anomaly Detection DatasetCode1
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language ModelsCode1
AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly DetectionCode1
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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