Building upon the foundational concepts and limitations discussed previously, this chapter examines the architectural innovations that have enabled significant improvements in the quality, stability, and controllability of Generative Adversarial Networks. Standard GANs often struggle with generating high-resolution images or maintaining training stability. Here, we study specific architectures designed to address these issues.

We will cover several key advancements:

• Progressive Growing (ProGAN): Learn how incrementally increasing the network resolution during training helps stabilize the process for high-definition image synthesis.
• Style-Based Generation (StyleGAN / StyleGAN2): Understand the mechanics of the style-based generator, including the mapping network and adaptive instance normalization (AdaIN), which provide enhanced control over generated image attributes.
• Scaling Up GANs (BigGAN): Investigate the techniques, such as orthogonal regularization and self-attention, that allow GANs to be trained effectively at larger scales, producing high-fidelity and diverse samples.
• Unpaired Image Translation (CycleGAN): Study the concept of cycle consistency loss, formulated as $L_{cyc}(G, F) = \mathbb{E}_{x \sim p_{data}(x)}[||F(G(x)) - x||_1] + \mathbb{E}_{y \sim p_{data}(y)}[||G(F(y)) - y||_1]$, enabling translation between domains without paired data.
• Attention Mechanisms: Analyze how incorporating self-attention layers helps models capture long-range spatial dependencies.

By dissecting these architectures, you will gain insight into the principles driving modern high-performance generative models. The chapter also includes practical implementation guidance for key components like those found in StyleGAN.