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 42514275 of 4856 papers

TitleStatusHype
Triggering Dark Showers with Conditional Dual Auto-EncodersCode0
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive LearningCode0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the EdgeCode0
Synthetic Data Generation for Anomaly Detection on Table GrapesCode0
Meta-survey on outlier and anomaly detectionCode0
Improved Anomaly Detection by Using the Attention-Based Isolation ForestCode0
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly DetectionCode0
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal SamplesCode0
Metric Learning for Novelty and Anomaly DetectionCode0
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic ImagesCode0
Detecting semantic anomaliesCode0
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical PerspectiveCode0
Importance Weighted Adversarial Discriminative Transfer for Anomaly DetectionCode0
Bump Hunting in Latent SpaceCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment PerspectiveCode0
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson LemmaCode0
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple RemedyCode0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature LearningCode0
syslrn: Learning What to Monitor for Efficient Anomaly DetectionCode0
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL DatasetCode0
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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