Generative machine learning models for image synthesis: Advances, challenges, and future directions

Generative adversarial networks (GANs) are popular models that can learn distributions and high-dimensional data such as images, audio, and texts. While these models have high representational capacity, training them can be difficult. Mode collapse, non-convergence, sensitivity to hyperparameters, and instability are among the most frequently mentioned issues. Several approaches have been proposed over the years, including new network architectures, alternative objective functions, and optimization techniques. This survey provides a detailed review of several GANs with their theoretical foundations, open problems, and challenges. Additionally, we focus on GAN-based image synthesis by covering widely used architectures such as DCGAN, pix2pix/CycleGAN, BigGAN, StyleGAN/StyleGAN2, and recent Transformer-based GANs (e.g., TransGAN), along with emerging hybrid directions. The key training issues and the development of optimization algorithms, from the most basic stochastic gradient descent to more sophisticated variants, are covered. The broad range of application areas is presented, and their stability and performance across different datasets are analyzed. We conclude by discussing current limitations and potential future improvements to more robust, efficient training methods. • Taxonomy of generative adversarial network architectures for image synthesis. • Training failures analyzed: mode collapse, non-convergence, and unstable dynamics. • Optimization compared: Wasserstein losses, gradient penalties, two time-scale updates. • Stabilization reviewed: feature matching, mini-batch diversity, self-attention. • Applications and open challenges for robust, efficient generative image models.