Implementing BigGAN

The BigGAN is an improved version of the SAGAN. The BigGAN ups the image resolution significantly from 128×128 to 512×512, and it does it without progressive growth of layers! The following are some sample images generated by BigGAN:

Figure 8.5 – Class-conditioned samples generated by BigGAN at 512x512 (Source: A. Brock et al., 2018, "Large Scale GAN Training for High Fidelity Natural Image Synthesis," https://arxiv.org/abs/1809.11096)

BigGAN is considered the state-of-the-art class-conditional GAN. We'll now look into the changes and modify the SAGAN code to make ourselves a BigGAN.

Scaling GANs

Older GANs tend to use small batch sizes as that would produce better-quality images. Now we know that the quality problem was caused by the batch statistics used in batch normalization, and this is addressed by using other normalization techniques. Still, the batch size has remained small as it is physically...