Building a SAGAN
The SAGAN has a simple architecture that looks like DCGAN's. However, it is a class-conditional GAN that uses class labels to both generate and discriminate between images. In the following figure, each image on each row is generated from different class labels:
Figure 8.3 – Images generated by a SAGAN by using different class labels. (Source: A. Brock et al., 2018, "Large Scale GAN Training for High Fidelity Natural Image Synthesis," https://arxiv.org/abs/1809.11096)
In this example, we will use the CIFAR10 dataset, which contains 10 classes of images with a resolution of 32x32. We will deal with the conditioning part later. Now, let's first complete the simplest part – the generator.
Building a SAGAN generator
At a high level, the SAGAN generator doesn't look very different from other GAN generators: it takes noise as input and goes through a dense layer, followed by multiple levels of upsampling...
