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Pytorch code to train image classifiers based on ODE Nets on MNIST and CIFAR-10, extract features and test robustness to adversarial examples

Python 98.06% Shell 1.94%
deep-learning feature-extraction image-classification ode-nets pytorch

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neural-ode-features's Issues

Wondering the best ODE results on CIFAR10 with Odenet of your tests.

Your code works seems really great!
I was confused about the experiments in that paper with MNIST dataset. And the best I can get so far is around 86.5% with ODEnet on CIFAR10. Wondering did you get anything over 90 with ODEnet through your works? I found your code today and it seems you tested way much things than I did.

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