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 (ICIAP '19) (pp. 432-442). Springer, Cham.

[2] Carrara, F., Amato, G., Falchi, F. and Gennaro, C., 2020, June. Continuous ODE-defined Image Features for Adaptive Retrieval. In Proceedings of the 2020 International Conference on Multimedia Retrieval (ICMR '20) (pp. 198-206). ACM.

[3] Carrara, F., Caldelli, R., Falchi, F. and Amato, G., 2019, December. On the robustness to adversarial examples of neural ode image classifiers. In 2019 IEEE International Workshop on Information Forensics and Security (WIFS '19) (pp. 1-6). IEEE.

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] and [2], run

./reproduce.sh

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

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To reproduce experiments described in [3], obtain the trained models, and then run

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

to attack a specific model and collect results.