A convolutional adversarial autoencoder implementation in pytorch using the WGAN with gradient penalty framework.
There's a lot to tweak here as far as balancing the adversarial vs reconstruction loss, but this works and I'll update as I go along.
The MNIST GAN seems to converge at around 30K steps, while CIFAR10 arguably doesn't output anything realistic ever (compared to ACGAN). Nonetheless it starts to looks ok at around 50K steps
The autoencoder components are able to output good reconstructions much faster than the GAN. ~10k steps on MNIST. The auto encoder is currently bad with CIFAR10 (under investigation)
There is a lot here that I want to add and fix with regard to image generation and large scale training.
But I can't do anything until pytorch fixes these issues with gradient penalty here
clearly need to fix this
To start training right away just run
start.sh
To train on MNIST
python3 train.py --dataset mnist --batch_size 50 --dim 32 -o 784
To train on CIFAR10
python3 train.py --dataset cifar10 --batch_size 64 --dim 32 -o 3072
For the wgan-gp components I mostly used caogang's nice implementation
Provide pretrained model files from on a google drive folder (push loader).
