Over the past few years, there has been growing interest in developing larger and deeper neural networks, including deep generative models like generative adversarial networks (GANs). However, GANs typically come with high computational complexity, leading researchers to explore methods for reducing the training and inference costs. One such approach gaining popularity in supervised learning is dynamic sparse training (DST), which maintains good performance while enjoying excellent training efficiency. Despite its potential benefits, applying DST to GANs presents challenges due to the adversarial nature of the training process. In this paper, we propose a novel metric called the balance ratio (BR) to study the balance between the sparse generator and discriminator. We also introduce a new method called balanced dynamic sparse training (ADAPT), which seeks to control the BR during GAN training to achieve a good trade-off between performance and computational cost. Our proposed method shows promising results on multiple datasets, demonstrating its effectiveness.

翻译：近年来，开发更大更深神经网络的兴趣持续增长，包括生成对抗网络（GANs）等深度生成模型。然而，GANs通常具有较高的计算复杂度，促使研究者探索降低训练与推理成本的方法。在监督学习中，动态稀疏训练（DST）因其在保持良好性能的同时兼具优异训练效率而日益流行。尽管具有潜在优势，但由于对抗训练过程的特殊性，将DST应用于GANs仍面临挑战。本文提出一种名为平衡比（BR）的新颖度量，用于研究稀疏生成器与判别器之间的平衡性。我们同时引入一种名为平衡动态稀疏训练（ADAPT）的新方法，该方法旨在控制GAN训练过程中的BR值，以在性能与计算成本之间实现良好权衡。所提方法在多个数据集上展现出令人期待的结果，验证了其有效性。