Resource-Aware GAN Training on Cloud Infrastructure for Large-Scale Image and Video Synthesis

Generative Adversarial Networks (GANs) have revolutionized the field of image and video synthesis, offering remarkable results in various domains such as entertainment, healthcare, and virtual reality.However, the computational demands of training GANs, particularly for large-scale image and video synthesis, pose significant challenges in terms of resource utilization, cost, and efficiency.Cloud computing infrastructure, with its scalable resources, has emerged as a promising solution to address these challenges.This paper explores the concept of resource-aware GAN training on cloud infrastructure, aiming to optimize the allocation and utilization of computational resources for efficient large-scale synthesis.We propose a resource-aware approach that dynamically adjusts cloud resources based on real-time training requirements, optimizing performance and reducing costs.Our approach is evaluated through extensive experiments on image and video synthesis tasks, demonstrating significant improvements in both computational efficiency and synthesis quality.The paper also discusses the potential of cloud-based resource optimization for future advancements in GAN training and synthesis applications.