Simplified Diagram of a GAN (Generative Adversarial Network)

This diagram illustrates the core adversarial training mechanism and data flow path of a Generative Adversarial Network (GAN). The process encompasses random noise input, the generator mapping low-dimensional vectors to pseudo-images via transposed convolution, the discriminator performing binary classification to distinguish between real and generated samples, and the complete closed loop where adversarial loss bidirectionally drives the alternating optimization of the generator and discriminator. The diagram clearly reveals the dynamic training logic from random sampling to image generation and then to the real-fake game, highlighting key design elements such as feature transformation, gradient flow, and loss constraints.