PyTorch VAE example extended with Autoregressive Quantile Networks (though, they aren't autoregressive when applied to VAE) https://arxiv.org/abs/1806.05575
By default, writes tensorboard logs to ./tensorboard, change with --log-folder /log/path. Saving just png images without tensorboard is not implemented.
With --conditioned argument AIQN network also receives class labels as inputs. VAE encoder / decoder does not have access to class labels.
PyTorch 0.4.0
tensorboard-pytorch https://github.com/lanpa/tensorboard-pytorch
Conditioned AIQN with KL regularizer main.py --conditioned --kl-scale 1 --log-folder ./tensorboard
No AIQN with KL regularizer (same as PyTorch example) main.py --no-aiqn --kl-scale 1 --log-folder ./tensorboard
AIQN / no AIQN without KL regularizer. Without AIQN and KL penalty generated images are mess. AIQN gives noticible improvement.
