Journal of Engineering Research

Generative Adversarial Networks (GANs) have shown significant potential in the field of image generation. This study explores the application of GANs in simple image generation, analyzing how network structure and training mechanisms influence the generation outcomes. The process involves a mutual game between the generator and discriminator, where parameters are optimized through adversarial training to learn and fit the distribution of simple image data. The study examines the generation performance under different activation functions and loss function combinations, using multi-layer perceptrons and convolutional neural networks as the basic models. Comparative experiments show that combining a convolutional structure with an appropriate loss function can significantly enhance the quality and diversity of image generation. The study also delves into issues such as mode collapse and gradient disappearance during training, along with their corresponding strategies, providing theoretical and practical references for future research on complex image generation. Additionally, it expands the application boundaries of GANs in basic computer vision tasks.

Generative adversarial networks; Simple image generation; Adversarial training; Image quality; Model optimization