Explore the interpretable semantics in Generative Adversarial Networks (GANs) through this comprehensive tutorial presented at CVPR'20 iMLCV. Delve into deep generative representation, GAN dissection for interpreting latent units, and the identification of causality in latent space. Learn about aligning latent space with attribute space, layer-wise stochasticity, and semantic hierarchy in GANs. Discover techniques for face synthesis, unsupervised attribute discovery, and GAN inversion for real faces. Gain insights into challenges and solutions in GAN inversion, extended latent codes, and image manipulation applications. Understand the concept of semantic diffusion and image processing with GAN prior, providing a comprehensive overview of cutting-edge research in interpretable vision and generative models.