Mechanisms of Generative Image-to-Image Translation Networks

• URL: http://arxiv.org/abs/2411.10368v1
• Date: Fri, 15 Nov 2024 17:17:46 GMT
• Title: Mechanisms of Generative Image-to-Image Translation Networks
• Authors: Guangzong Chen, Mingui Sun, Zhi-Hong Mao, Kangni Liu, Wenyan Jia,
• Abstract summary: We propose a streamlined image-to-image translation network with a simpler architecture compared to existing models. We show that adversarial for GAN models yields results comparable to those of existing methods without additional complex loss penalties.
• Score: 1.602820210496921
• License:
• Abstract: 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.

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