Building upon the GAN fundamentals reviewed previously, this chapter examines several influential advancements in Generative Adversarial Network design. While basic GANs provide a foundation, they often face limitations regarding output resolution, training stability, and control over the generation process. Here, we will study architectures specifically developed to address these challenges.

We will cover:

• Progressive Growing of GANs (ProGAN): A technique enabling stable training for high-resolution image synthesis by incrementally increasing network depth.
• Style-Based Generators (StyleGAN variants): Models offering enhanced control over image attributes through techniques like style mixing and adaptive instance normalization (AdaIN).
• Unpaired Image-to-Image Translation (CycleGAN): An approach using cycle-consistency loss ($L_{\text{cyc}}$) for domain adaptation without requiring paired training examples.

Furthermore, we will investigate methods for generating data conditioned on specific inputs, the incorporation of attention mechanisms to capture long-range dependencies, and techniques for analyzing and manipulating the latent space $z$ to influence the generated output. A hands-on practical section focuses on implementing core components of the StyleGAN architecture.