Mechanisms of Generative Image-to-Image Translation Networks

Existing image-to-image translation models often rely on complex architectures with multiple loss terms, making them difficult to interpret and computationally expensive. This paper is motivated by the need for a simpler, more fundamental understanding of the underlying mechanisms in image-to-image translations. We use a streamlined Generative Adversarial Network (GAN) that eliminates the need for auxiliary loss functions, such as cycle consistency or identity loss, which are common in state-of-the-art models. Our primary contribution is a theoretical and experimental demonstration that a basic GAN architecture is sufficient for high-quality image-to-image translation. We establish a connection between GANs and autoencoders, providing a clear rationale for how adversarial training alone can preserve content while transforming style. To validate our approach, we conduct experiments on several benchmark datasets and evaluate the performance of our simplified model, which achieves comparable results to more complex architectures. Our work demystifies the role of adversarial loss and offers a more efficient and interpretable framework for image-to-image translation.