Code for the ICLR submission Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer.
sudo apt install virtualenv
cd <path_to_code>
virtualenv --system-site-packages env2
. env2/bin/activate
pip install -r requirements.txt
Choose a folder where to save the datasets, for example ~/Data
export AE_DATA=~/Data
python create_datasets.py
CUDA_VISIBLE_DEVICES=0 python acai.py \
--train_dir=TEMP \
--latent=16 --latent_width=2 --depth=16 --dataset=celeba32
All training from the paper can be found in folder runs
.
These are the maintained models:
- aae.py
- acai.py
- baseline.py
- denoising.py
- dropout.py
- vae.py
- vqvae.py
- classifier_fc.py: fully connected single layer from raw pixels, see
runs/classify.sh
for examples. - Auto-encoder classification is trained at the same as the auto-encoder.
- cluster.py: K-means clustering, see
runs/cluster.sh
for examples.
- create_datasets.py: see Installing datsets for more info.