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 1201–1250 of 4856 papers

TitleStatusHype
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly DetectionCode0
Anomaly Detection in Graph Structured Data: A Survey—0
TS3IM: Unveiling Structural Similarity in Time Series through Image Similarity Assessment Insights—0
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies—0
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRICode1
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting MaskCode2
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis—0
Less-supervised learning with knowledge distillation for sperm morphology analysisCode0
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection—0
Supervised Anomaly Detection for Complex Industrial ImagesCode1
Anomaly Detection in Certificate Transparency Logs—0
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI—0
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series—0
Braced Fourier Continuation and Regression for Anomaly DetectionCode0
AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection—0
A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series—0
A self-supervised text-vision framework for automated brain abnormality detection—0
GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning—0
Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?Code1
A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System—0
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems—0
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly DetectionCode0
Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly DetectionCode1
Subgraph2vec: A random walk-based algorithm for embedding knowledge graphs—0
Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI—0
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video AnomalyCode2
Improved AutoEncoder with LSTM module and KL divergenceCode0
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer StudyCode0
An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory PredictionCode0
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-ADCode0
Self-supervised learning for classifying paranasal anomalies in the maxillary sinusCode0
A Survey on Diffusion Models for Time Series and Spatio-Temporal DataCode4
Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment—0
Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent NeighborhoodsCode0
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling—0
Accurate and fast anomaly detection in industrial processes and IoT environments—0
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and BeyondCode0
Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows—0
Synchronized Stepwise Control of Firing and Learning Thresholds in a Spiking Randomly Connected Neural Network toward Hardware Implementation—0
Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography ImagesCode1
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection—0
Anomaly Detection for Incident Response at Scale—0
ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk Assessment—0
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries—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