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
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RotNet-OOD
python test.py --method=rot --ood_dataset=cifar100
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baseline
python test.py --method=msp --ood_dataset=svhn
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- Metric : AUROC
CIFAR-100 | SVHN | |
---|---|---|
Maximum Softmax Probability (baseline) |
0.6986 | 0.7190 |
RotNet | 0.7931 | 0.9584 |
RotNet (rot loss only) | 0.7132 | 0.9560 |
RotNet (KL divergence only) | 0.7834 | 0.8522 |
[1] full code(by authors): https://github.com/hendrycks/ss-ood
[2] Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty(NeurIPS 2019): https://arxiv.org/abs/1906.12340
[3] A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks(ICLR 2017): https://arxiv.org/abs/1610.02136