Batch Norm with Entropic Regularization Turns Deterministic Autoencoders into Generative Models
Abstract
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input. The encoder achieves this by sampling from a distribution for every input, instead of outputting a deterministic code per input. The great advantage of this process is that it allows the use of the network as a generative model for sampling from the data distribution beyond provided samples for training. We show in this work that utilizing batch normalization as a source for non-determinism suffices to turn deterministic autoencoders into generative models on par with variational ones, so long as we add a suitable entropic regularization to the training objective.
Cite
Text
Ghose et al. "Batch Norm with Entropic Regularization Turns Deterministic Autoencoders into Generative Models." Uncertainty in Artificial Intelligence, 2020.Markdown
[Ghose et al. "Batch Norm with Entropic Regularization Turns Deterministic Autoencoders into Generative Models." Uncertainty in Artificial Intelligence, 2020.](https://mlanthology.org/uai/2020/ghose2020uai-batch/)BibTeX
@inproceedings{ghose2020uai-batch,
title = {{Batch Norm with Entropic Regularization Turns Deterministic Autoencoders into Generative Models}},
author = {Ghose, Amur and Rashwan, Abdullah and Poupart, Pascal},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2020},
pages = {1079-1088},
volume = {124},
url = {https://mlanthology.org/uai/2020/ghose2020uai-batch/}
}