To extend or adapt the proposed Gauss-Newton-based method for GAN optimization to handle more complex architectures or diverse datasets, several strategies can be considered. One approach is to explore different preconditioning techniques or modifications to the Gauss-Newton method to improve its scalability and efficiency. For example, incorporating adaptive learning rates or momentum terms can enhance the method's ability to navigate high-dimensional parameter spaces and capture complex data distributions.
Furthermore, the method can be extended by integrating techniques from other optimization algorithms, such as natural gradient methods or implicit regularization. By combining the strengths of different optimization approaches, the proposed method can potentially achieve better convergence rates, improved stability, and enhanced performance on a wider range of datasets.
Additionally, exploring the application of the Gauss-Newton approach in conjunction with advanced GAN architectures, such as progressive growing GANs or transformer-based models, can help leverage the method's strengths in capturing higher-order information and improving the quality of generated samples. By adapting the method to handle more diverse architectures and datasets, its applicability and effectiveness in GAN training can be significantly enhanced.