/RotNet-OOD

Self-Supervised Learning for OOD Detection (NeurIPS 2019)

Primary LanguagePython

Self-Supervised Learning for OOD Detection

A Simplified Pytorch implementation of Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty.

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Results

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These results show that self-supervised auxiliary loss improves model robustness in terms of the Out-of-Distribution detection.

The code supports only Multi-class OOD Detection experiment(in-dist: CIFAR-10, out-of-dist: CIFAR-100/SVHN).

Usage

  • RotNet-OOD

    python test.py --method=rot --ood_dataset=cifar100

  • baseline

    python test.py --method=msp --ood_dataset=svhn