生成式图像到图像翻译网络的机制
Mechanisms of Generative Image-to-Image Translation Networks
AI总结：
本文提出架构更简单的生成式图像到图像翻译网络，研究GANs与自动编码器关系，解释仅用GAN组件的有效性，证明无额外复杂损失惩罚的对抗性训练结果可比现有方法，并阐明原理、验证发现。
摘要：
生成对抗网络（GANs）是一类已广泛应用于图像到图像翻译领域的神经网络。本文提出了一种简化的图像到图像翻译网络，其架构比现有模型更简单。我们研究了GANs与自动编码器之间的关系，并解释了仅使用GAN组件处理图像翻译任务的有效性。我们表明，GAN模型的对抗性训练在不添加额外复杂损失惩罚的情况下，能产生与现有方法相当的结果。随后，我们阐明了这一现象背后的原理。我们还纳入实验结果以证明我们发现的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
