To be presented at the Adversarial Training Workshop at NIPS 2016, Barcelona.
Arxiv link will be posted in the near future!
Please cite our work:
Pedro Tabacof, Julia Tavares, and Eduardo Valle. Adversarial Images for Variational Autoencoders. Adversarial Training Workshop, NIPS. 2016.
To reproduce our experiments, simply run the notebooks.
There are some options that can be readily changed, the most important one being do_train_model: Set it to True to train the model from scratch, or to False to use the pretrained models in the params folder.
adv: Adversarial images for (variational) autoencoders
clf: Adversarial images for classifier experiments
ae: Deterministic autoencoders
vae: Variational autoencoders
mnist: MNIST dataset
svhn: SVHN dataset
params: Pretrained AEs, VAEs and CLFs
results: folder with CSVs containing the experiments results -- to be used for plotting
results.ipynb: Plotting results from the folder above