/AAE-PyTorch

Adversarial autoencoder (basic/semi-supervised/supervised)

Primary LanguagePython

AAE-PyTorch

Adversarial autoencoder (basic/semi-supervised/supervised)

First,

$ python create_datasets.py

Then, you get data/MNIST, data/subMNIST (atomatically downloded in data/ directory), which are MNIST image datasets. you also get train_labeled.p, train_unlabeled.p, validation.p, which are list of tr_l, tr_u, tt image.

Second,

$ python aae_baisc.py

or

$ python aae_supervised.py

You can get category conditional images like below,

$ python aae_semi_supervised.py