PyTorch re-implementation of Auto-Encoding Variational Bayes by Kingma et al. 2013.
MINST http://yann.lecun.com/exdb/mnist/
FreyFace https://cs.nyu.edu/~roweis/data.html
save to './datasets/'
or specify the arguments
python ./src/VAE_train.py \
--data='FreyFace' \
--data_path='./datasets/FreyFace' \
--batch_size=100 \
--latent_dim=10 \
--hidden_dim=200 \
--learning_rate=0.01 \
--epoch=10000 \
--output_dir='./output'
python ./src/VAE_train.py -h
@ARTICLE{2013arXiv1312.6114K,
author = {{Kingma}, Diederik P and {Welling}, Max},
title = "{Auto-Encoding Variational Bayes}",
journal = {arXiv e-prints},
keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
year = "2013",
month = "Dec",
eid = {arXiv:1312.6114},
pages = {arXiv:1312.6114},
archivePrefix = {arXiv},
eprint = {1312.6114},
primaryClass = {stat.ML},
adsurl = {https://ui.adsabs.harvard.edu/abs/2013arXiv1312.6114K},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}