OFFICIAL REPOSITORY IS HERE.
This is not official and does not reproduce/implement all the experiments and results.
Adversarially Learned Inference with Conditional Entropy.
This repository has 2 kinds of experiments on GMM dataset.
- Python 3.6.5
- matplotlib
- pytorch 0.4.1
- tqdm (progress bar)
python3 train_ALICE_toydata.py
: Train using explicit cyclic consistencypython3 train_ALICE_toydata.py --adv
: Train using implicit cyclic consistency
--easy
option reduces the number of modes in dataset X
.
Results and the used dataset saved under args.results_dir/{YYMMDD-HMS}ALICE_unsupervised_{MSE or adversarial}_reconstruction
Datasets are saved [x, z]_trn.pkl using torch.save method.
Some results are under results
.
I emprically confirmed --n_dis
had some effects on results though, training in this setting was not stable in general.
- Experiments on toy-dataset (GMM)