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 3351–3400 of 4856 papers

TitleStatusHype
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditionsCode1
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans—0
Heart Sound Classification Considering Additive Noise and Convolutional Distortion—0
Heterogeneous Noisy Short Signal Camouflage in Multi-Domain Environment Decision-Making—0
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
IoT Solutions with Multi-Sensor Fusion and Signal-Image Encoding for Secure Data Transfer and Decision Making—0
Analysis of Vision-based Abnormal Red Blood Cell Classification—0
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly SegmentationCode1
CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System—0
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance SacrificeCode0
Shell Theory: A Statistical Model of RealityCode0
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron ColliderCode0
A Survey on Anomaly Detection for Technical Systems using LSTM Networks—0
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Performance Analysis of a Foreground Segmentation Neural Network Model—0
CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study—0
Finite sample guarantees for quantile estimation: An application to detector threshold tuningCode0
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
Deep Visual Anomaly detection with Negative Learning—0
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly DetectionCode1
Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss—0
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial NetworksCode0
Multi-Perspective Anomaly Detection—0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
Graph Sanitation with Application to Node Classification—0
Masked Contrastive Learning for Anomaly DetectionCode1
StackVAE-G: An efficient and interpretable model for time series anomaly detectionCode0
Vision Transformers are Robust LearnersCode1
DOC3-Deep One Class Classification using Contradictions—0
Understanding the Effect of Bias in Deep Anomaly DetectionCode0
Theoretical Concept Study of Cooperative Abnormality Detection and Localization in Fluidic-Medium Molecular Communication—0
Importance Weighted Adversarial Discriminative Transfer for Anomaly DetectionCode0
DoS and DDoS Mitigation Using Variational Autoencoders—0
Exploring the Intrinsic Probability Distribution for Hyperspectral Anomaly Detection—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing—0
Anomaly Detection via Controlled Sensing and Deep Active Inference—0
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance VideoCode1
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow—0
Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: a case study in the capitals of Brazil—0
Unsupervised Offline Changepoint Detection EnsemblesCode1
Good Practices and A Strong Baseline for Traffic Anomaly DetectionCode0
Energy-Based Anomaly Detection and Localization—0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine LearningCode0
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs—0
Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings—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
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