Adversarially Constrained Autoencoder Interpolations (ACAI)

Code for the ICLR submission Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer.

Setup

Config with virtualenv

sudo apt install virtualenv

cd <path_to_code>
virtualenv --system-site-packages env2
. env2/bin/activate
pip install -r requirements.txt

Config environment variables

Choose a folder where to save the datasets, for example ~/Data

export AE_DATA=~/Data

Installing datasets

python create_datasets.py

Training

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.

Models

These are the maintained models:

• aae.py
• acai.py
• baseline.py
• denoising.py
• dropout.py
• vae.py
• vqvae.py

Classifiers / clustering

• 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.

Utilities

• create_datasets.py: see Installing datsets for more info.