Accueil Plateforme Docs Actualités Articles de recherche Tutoriels Ensembles de données Wiki SOTA Modèles LLM Classement GPU Événements

Français

Anomaly Detection On One Class Cifar 100

Métriques

AUROC

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title	Repository
GeneralAD	98.4	GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
Transformaly	97.7	Transformaly -- Two (Feature Spaces) Are Better Than One
PANDA-OE	97.3	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Mean-Shifted Contrastive Loss	96.5	Mean-Shifted Contrastive Loss for Anomaly Detection
PANDA	94.1	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
CSI	89.6	CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
GAN based Anomaly Detection in Imbalance Problems	87.4	GAN-based Anomaly Detection in Imbalance Problems	-
DisAug CLR	86.5	Learning and Evaluating Representations for Deep One-class Classification
DUIAD	86	Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework	-
Rotation Prediction	84.1	Learning and Evaluating Representations for Deep One-class Classification
MTL	83.95	Shifting Transformation Learning for Out-of-Distribution Detection	-
Self-Supervised Multi-Head RotNet	80.1	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Geom	78.7	Deep Anomaly Detection Using Geometric Transformations
Self-Supervised DeepSVDD	67	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Self-Supervised One-class SVM, RBF kernel	62.6	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

0 of 15 row(s) selected.