Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration

The training of Generative Adversarial Networks (GANs) for high-fidelity images has predominantly relied on large-scale datasets. Emerging research, particularly on GANs 'lottery tickets', suggests that dense GANs models have sparse sub-networks capable of superior performance with limited data. However, the conventional process to uncover these 'lottery tickets' involves a resource-intensive train-prune-retrain cycle. Addressing this, our paper introduces Re-GAN, a novel, data-efficient approach for GANs training that dynamically reconfigures the GANs architecture during training. This method focuses on iterative pruning of non-important connections and regrowing them, thereby preventing premature loss of important features and maintaining the model's representational strength. Re-GAN provides a more stable and efficient solution for GANs models with limited data, offering an alternative to existing progressive growing methods and GANs tickets. While Re-GAN has already demonstrated its potential in image generation across diverse datasets, domains, and resolutions, in this paper, we significantly expand our study. We incorporate new applications, notably Image-to-Image translation, include additional datasets, provide in-depth analyses, and explore compatibility with data augmentation techniques. This expansion not only broadens the scope of Re-GAN but also establishes it as a generic training methodology, demonstrating its effectiveness and adaptability in different GANs scenarios.