Generative Adversarial Networks (GANs) are a class of neural networks that have been widely used in the field of image-to-image translation. In this paper, we propose a streamlined image-to-image translation network with a simpler architecture compared to existing models. We investigate the relationship between GANs and autoencoders and provide an explanation for the efficacy of employing only the GAN component for tasks involving image translation. We show that adversarial for GAN models yields results comparable to those of existing methods without additional complex loss penalties. Subsequently, we elucidate the rationale behind this phenomenon. We also incorporate experimental results to demonstrate the validity of our findings.

翻译：生成对抗网络（GAN）是一类在图像到图像转换领域得到广泛应用的神经网络。本文提出了一种结构简化的图像到图像转换网络，其架构相较于现有模型更为简洁。我们探究了GAN与自编码器之间的关系，并对仅使用GAN组件完成图像转换任务的有效性提供了解释。研究表明，GAN模型的对抗性训练能够取得与现有方法相当的结果，而无需引入额外的复杂损失惩罚项。随后，我们阐明了这一现象背后的原理。我们还通过实验结果验证了所提结论的有效性。