This video (part 3 of a series) discusses GAN (Generative Adversarial Network) improvements for high-precision image generation. It focuses on loss function optimization and architecture refinements to overcome challenges like gradient vanishing. Key improvements include using hinge loss or least squares loss to replace the original minimax loss, and employing techniques like gradient penalty or spectral normalization to stabilize training and improve generator performance. This segment details the challenges of gradient vanishing in GANs and introduces solutions like hinge loss and Wasserstein loss. It explains how these alternative loss functions address the issue of the generator's inability to receive sufficient gradients for updating when the discriminator becomes too accurate, leading to improved generator training and higher-quality image generation. The explanation includes a clear description of the mathematical concepts and their practical implications.