A preliminary exploration of generative adversarial networks (GANs) in simple image generation
GAN生成对抗网络在简单图像生成中的初步探索

摘要

生成对抗网络（GAN）在图像生成领域展现强大潜力。探索其在简单图像生成的应用，分析网络结构与训练机制对生成效果的影响。生成器与判别器相互博弈的过程，通过对抗训练优化参数，实现对简单图像数据分布的学习与拟合。研究不同激活函数、损失函数组合下的生成性能，采用多层感知机与卷积神经网络构建基础模型，对比实验结果表明，卷积结构结合合适损失函数能显著提升图像生成质量与多样性。详细剖析训练过程中的模式崩溃、梯度消失等问题及应对策略，为后续复杂图像生成研究提供理论与实践参考，同时拓展 GAN 在计算机视觉基础任务中的应用边界。

Abstract

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.