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Anomaly Detection On One Class Cifar 100

Métriques

AUROC

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUROC	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
DisAug CLR	86.5	Learning and Evaluating Representations for Deep One-class Classification
PANDA-OE	97.3	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Self-Supervised Multi-Head RotNet	80.1	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
GAN based Anomaly Detection in Imbalance Problems	87.4	GAN-based Anomaly Detection in Imbalance Problems	-
DUIAD	86	Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework	-
Geom	78.7	Deep Anomaly Detection Using Geometric Transformations
Self-Supervised One-class SVM, RBF kernel	62.6	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Self-Supervised DeepSVDD	67	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Mean-Shifted Contrastive Loss	96.5	Mean-Shifted Contrastive Loss for Anomaly Detection
Rotation Prediction	84.1	Learning and Evaluating Representations for Deep One-class Classification
MTL	83.95	Shifting Transformation Learning for Out-of-Distribution Detection	-
CSI	89.6	CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
PANDA	94.1	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

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