In the same spirit as InfoGAN, StackedGAN proposes a method for disentangling latent representations for conditioning generator outputs. However, StackedGAN uses a different approach to the problem. Instead of learning how to condition the noise to produce the desired output, StackedGAN breaks down a GAN into a stack of GANs. Each GAN is trained independently in the usual discriminator-adversarial manner with its own latent code.
Figure 6.2.1 shows us how StackedGAN works in the context of the hypothetical celebrity face generation. Assuming that the Encoder network is trained to classify celebrity faces.
The Encoder network is made of a stack of simple encoders, Encoder i where i = 0 … n - 1 corresponding to n features. Each encoder extracts certain facial features. For example, Encoder0 may be the encoder for hairstyle features, Features1. All the simple encoders contribute to making the overall Encoder perform correct predictions.
The idea behind StackedGAN is that if we would...
