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SOTA
分布外检测
Out Of Distribution Detection On Cifar 10 Vs
Out Of Distribution Detection On Cifar 10 Vs
评估指标
AUROC
FPR95
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
AUROC
FPR95
Paper Title
Repository
DHM
100
-
Deep Hybrid Models for Out-of-Distribution Detection
-
R+ViT finetuned on CIFAR-10
98.52
-
Exploring the Limits of Out-of-Distribution Detection
ViT finetuned on CIFAR-10
98.42
-
Exploring the Limits of Out-of-Distribution Detection
MLP-Mixer finetuned on CIFAR-10
97.85
-
Exploring the Limits of Out-of-Distribution Detection
Forte
97.63 ± 00.15
-
Forte : Finding Outliers with Representation Typicality Estimation
ZODE-KNN
97.12
18.29
Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
ERD (ResNet18)
95.1
-
Semi-supervised novelty detection using ensembles with regularized disagreement
OpenHybrid
95.1
-
Hybrid Models for Open Set Recognition
-
Wide 40-2 + OECC
94.9
-
Outlier Exposure with Confidence Control for Out-of-Distribution Detection
WRN 40-2 + OE
93.3
-
Deep Anomaly Detection with Outlier Exposure
Isolation Forest on EfficientNet Softmax values
91.95
-
Out-of-Distribution Detection Using Outlier Detection Methods
WRN 40-2 + Rotation Prediction
90.9
-
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
WRN 40-2 (MSP Baseline)
87.9
-
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
ResNet + diagonal elements of Gram matrix
79.7
-
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
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Out Of Distribution Detection On Cifar 10 Vs | SOTA | HyperAI超神经