Progressive Growing of Generative Adversarial Networks (GANs) is a training methodology that is introduced in a context where high-resolution image synthesis was dominated by autoregressive models, such as PixelCNN and Variational Autoencoders (VAEs) – just like the models used in the paper Improved Variational Inference with Inverse Autoregressive Flow (https://arxiv.org/abs/1606.04934).
As we described in earlier chapters, although autoregressive models are able to produce high-quality images, when compared to their counterparts they lack an explicit latent representation that can be directly manipulated. Additionally, due to their autoregressive nature, at the time of inference autoregressive models tend to be slower than their counterparts. On the other hand, VAE-based models have quicker inference but are harder to train, and the VAE-based...
