Awesome
Self-Supervised Learning for OOD Detection
A Simplified Pytorch implementation of Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty(NeurIPS 2019)
The code supports only Multi-class OOD Detection experiment(in-dist: CIFAR-10, Out-of-dist: CIFAR-100/SVHN)
- Command
python3 test.py
- Result
- Metric : AUROC
| Dataset \ Method | RotNet | MSP |
|---|---|---|
| CIFAR100 | 0.8310 | 0.7695 |
| SVHN | 0.9755 | 0.8747 |
- Reference
- full code(by authors): https://github.com/hendrycks/ss-ood
- Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty(NeurIPS 2019): https://arxiv.org/abs/1906.12340
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks(ICLR 2017): https://arxiv.org/abs/1610.02136