Neural ODE Image Classifiers

Pytorch code for training and evaluating Neural ODEs image classifiers on MNIST and CIFAR-10 datasets. It reproduces experiments presented in the following papers:

[1] Carrara, F., Amato, G., Falchi, F. and Gennaro, C., 2019, September. Evaluation of Continuous Image Features Learned by ODE Nets. In International Conference on Image Analysis and Processing (pp. 432-442). Springer, Cham.

[2] Carrara, F., Caldelli, R., Falchi, F. and Amato, G., 2019, December. On the Robustness to Adversarial Examples of Neural ODE Image Classifiers. Accepted at the 2019 IEEE International Workshop on Information Forensics and Security.

Getting Started

Clone and install requirements:

git clone --recursive https://github.com/fabiocarrara/neural-ode-features.git
cd neural-ode-features
pip install -e torchdiffeq
pip install torchvision foolbox h5py pandas tqdm seaborn sklearn

Reproduce Experiments

To obtain the trained models and reproduce the experiments described in [1], run

./reproduce.sh

Pre-trained models are also available: neural-ode-features-runs.zip (172MB)

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To reproduce experiments described in [2], obtain the trained models, and then run

cd adversarial
./reproduce.sh <path/to/specific_run_folder>

to attack a specific model and collect results.