Out Of Distribution Detection On Cifar 10
Metrics
AUROC
Results
Performance results of various models on this benchmark
| Paper Title | Code | ||
|---|---|---|---|
| DHM | 100 | Deep Hybrid Models for Out-of-Distribution Detection | |
| Wide ResNet 40x2 | 99.9 | An Effective Baseline for Robustness to Distributional Shift | |
| ResNet 34 + OECC+GM | 99.7 | Outlier Exposure with Confidence Control for Out-of-Distribution Detection | |
| ResNet 34 + FSSD | 99.5 | Feature Space Singularity for Out-of-Distribution Detection | |
| Wide ResNet 40x2 | 99.43 | RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection | |
| ZODE-KNN | 99.12 | Boosting Out-of-Distribution Detection with Multiple Pre-trained Models | |
| ResNet18 + APR-P | 98.1 | Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain | |
| WRN 40-2 (MSP Baseline) | 97.8 | Deep Anomaly Detection with Outlier Exposure | |
| WRN 40-2 + OE | 97.8 | Deep Anomaly Detection with Outlier Exposure | |
| WRN 40-2 + Rotation Prediction | 96.2 | Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty |
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