<![CDATA[Generative Adversarial Networks (GANs) have become a prominent approach for image generation; however, they often suffer from training instability, mode collapse, and limited controllability of generated outputs. This study investigates the role of mutual information in improving generative modeling through a comparative analysis of Vanilla GAN, Conditional GAN (CGAN), and InfoGAN. Experiments were conducted using two image datasets with different levels of complexity, namely MNIST and Anime Face, under comparable training configurations. The evaluation focused on training behavior, convergence characteristics, generated image quality, and latent representation learning. The results revealed notable differences among the evaluated models. Vanilla GAN exhibited unstable convergence behavior at higher training epochs, while CGAN provided conditional control over generated outputs but did not fully mitigate training instability. In contrast, InfoGAN maintained more balanced generator and discriminator loss dynamics and produced visually consistent outputs across both datasets. Furthermore, latent code manipulation experiments showed that InfoGAN learned more structured and interpretable latent representations, enabling controllable feature variation in generated images. These findings indicate that incorporating mutual information improves representation learning, controllability, and training stability in GAN-based image generation. This study provides an empirical comparison of GAN, CGAN, and InfoGAN under a unified experimental framework and demonstrates that mutual information regularization contributes to improved training stability, controllable generation, and more interpretable latent representations. The findings highlight the potential of information-theoretic regularization for enhancing generative modeling performance.]]>
