A Survey on Recent Advancements in Lightweight Generative Adversarial Networks, their Applications and Datasets

Generative Adversarial Networks (GANs) have garnered significant research attention owing to their revolutionary generator-vs-discriminator architecture, making them versatile for various domains, including medical, military, and computer vision applications. Nevertheless, their computationally demanding nature during training and inference restricts their widespread adoption on mobile and edge devices. In this study, the latest advancements are explored in lightweight GAN implementations, considering their unique characteristics and diverse applications. The objective is to identify modifications that can enhance the efficiency of GAN-based models without compromising their robustness and accuracy, both for specific use-cases and in a more general context. Additionally, a discussion is presented on the availability of datasets suitable for lightweight GAN training and evaluation, as well as potential research directions for the future.