Enhancing GANs With MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition
Generative Adversarial Networks (GANs) have gained considerable attention owing to their impressive ability to generate high-quality, realistic images from a desired data distribution.This research introduces advancements in GANs by developing an improved activation function, a novel training strategy, and an adaptive rank decomposition method to c