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SOTA
異常検出
Anomaly Detection On One Class Cifar 100
Anomaly Detection On One Class Cifar 100
評価指標
AUROC
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
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
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
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