Generative adversarial networks (GANs) are capable of generating strikingly realistic samples but state-of-the-art GANs can be extremely computationally expensive to train. In this paper, we propose the fused propagation (FusedProp) algorithm which can be used to efficiently train the discriminator and the generator of common GANs simultaneously using only one forward and one backward propagation. We show that FusedProp achieves 1.49 times the training speed compared to the conventional training of GANs, although further studies are required to improve its stability. By reporting our preliminary results and open-sourcing our implementation, we hope to accelerate future research on the training of GANs.

中文翻译：

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FusedProp：面向生成对抗网络的高效训练

生成对抗网络 (GAN) 能够生成非常逼真的样本，但最先进的 GAN 的训练计算成本可能非常高。在本文中，我们提出了融合传播 (FusedProp) 算法，该算法可用于仅使用一种前向传播和一种后向传播同时有效地训练常见 GAN 的鉴别器和生成器。我们表明，与 GAN 的常规训练相比，FusedProp 实现了 1.49 倍的训练速度，尽管需要进一步研究以提高其稳定性。通过报告我们的初步结果和开源我们的实施，我们希望加速 GAN 训练的未来研究。