Exploring Generative Models: GANs vs. VAEs

This chapter examines the intricacies of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), focusing on their strengths and shortcomings in generating detailed imagery and scene manipulation. It discusses the challenges of training these models, including mode collapse and the trade-off between understanding rough shapes and intricate details. Additionally, the chapter touches on practical considerations in training, emphasizing the importance of closing the domain gap between synthetic and real data.