This package includes the implementation of my ICML 2020 work "Learning Autoencoders with Relational Regularization" [https://arxiv.org/pdf/2002.02913.pdf]
- argparse
- matplotlib
- numpy
- pickle
- pytorch
- sklearn
We test this example in a conda environment on Windows 10, with cuda 10.1 and one 1080Ti GPU
- Open a terminal and go to the folder of the example.
- python test_rae.py --model-type deterministic --source-data DATANAME (Learning a deterministic RAE for a dataset.)
- python test_rae.py --model-type probabilistic --source-data DATANAME (Learning a probabilistic RAE for a dataset.)
- The DATANAME can be MNIST and CelebA
- Open a terminal and go to the folder of the example.
- python test_MODEL.py --source-data DATANAME
- The MODEL can be vae, wae, swae, gmvae, and vampprior
All the results are in the folder "Results".