Mechanisms of Generative Image-to-Image Translation Networks

TL;DR

This paper introduces a simplified GAN-based image-to-image translation network, explaining its effectiveness and demonstrating that it performs comparably to more complex models without extra loss functions.

Contribution

It presents a streamlined architecture for image translation and clarifies why using only GANs can be effective, reducing complexity in model design.

Findings

Simpler architecture achieves comparable results to complex models.

Using only GAN components is effective for image translation.

Experimental results validate the proposed approach.

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.