cDCGAN

Pytorch implementation of cDCGAN.
During my studies I had a lot of trouble finding a cDCGAN architecture that worked as I expected, so I decided to write my own version, finding an alternative way to condition it.
Nothing particularly new, but it can be useful for educational purposes.
This was used as a comparison with our meta-DCGAN-{1,2} MLP and meta-WGAN-gp 2MLP architectures

Architecture

Example

(could you recognize that these numbers were drawn by a machine? 😁)

Metrics

MNIST:

FID: 11.927998813352
IS: (9.74927, 0.26216) (max 10; InceptionV3 trained with MNIST)

CIFAR10:

FID: 35.16453
IS: (3.86256, 0.14062)

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See also my cWGAN-gp