Image-To-Image Translation: A Comprehensive Study on the Efficacy of Pix2Pix GAN in Producing High-Quality Visual Transformations
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| Formato: | Recurso digital |
| Lenguaje: | Idioma anglosajón |
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2025
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| _version_ | 1866902053690802176 |
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| author | Dhakaa Mohsin Kareem |
| author_facet | Dhakaa Mohsin Kareem |
| contents | <p>Image-to-image translation poses a significant challenge, garnering substantial research attention in recent years. This study aims to devise a versatile image-to-image translation method capable of producing superior images with minimal user intervention. The proposed solution, the Pix2Pix GAN, is a Generative Adversarial Network (GAN) designed to translate images seamlessly between different domains. The Pix2Pix GAN comprises two integral components: a generator and a discriminator. The generator's role is to produce images originating from the source domain, while the discriminator is tasked with distinguishing between real and generated images. Both networks undergo adversarial training, engaging in a competitive dynamic. The generator strives to create images that are indistinguishable from real ones, while the discriminator endeavors to identify disparities between real and generated images. The effectiveness of the Pix2Pix GAN was assessed across various image-to-image translation scenarios, such as transitioning from day to night, converting sketches to photos, and transforming paintings into photographs. The Pix2Pix GAN consistently demonstrated the capability to generate high-quality images across these tasks, requiring minimal user input. In conclusion, the Pix2Pix GAN presents a promising paradigm for image-to-image translation. Its capacity to produce top-tier images with minimal user intervention establishes it as a robust solution applicable to a diverse array of image translation tasks.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18280002 |
| institution | Zenodo |
| language | ang |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Image-To-Image Translation: A Comprehensive Study on the Efficacy of Pix2Pix GAN in Producing High-Quality Visual Transformations Dhakaa Mohsin Kareem Generative Adversarial Networks (GANs), Image-to-Image Translation, Conditional GANs, Unsupervised Learning, Adversarial Loss <p>Image-to-image translation poses a significant challenge, garnering substantial research attention in recent years. This study aims to devise a versatile image-to-image translation method capable of producing superior images with minimal user intervention. The proposed solution, the Pix2Pix GAN, is a Generative Adversarial Network (GAN) designed to translate images seamlessly between different domains. The Pix2Pix GAN comprises two integral components: a generator and a discriminator. The generator's role is to produce images originating from the source domain, while the discriminator is tasked with distinguishing between real and generated images. Both networks undergo adversarial training, engaging in a competitive dynamic. The generator strives to create images that are indistinguishable from real ones, while the discriminator endeavors to identify disparities between real and generated images. The effectiveness of the Pix2Pix GAN was assessed across various image-to-image translation scenarios, such as transitioning from day to night, converting sketches to photos, and transforming paintings into photographs. The Pix2Pix GAN consistently demonstrated the capability to generate high-quality images across these tasks, requiring minimal user input. In conclusion, the Pix2Pix GAN presents a promising paradigm for image-to-image translation. Its capacity to produce top-tier images with minimal user intervention establishes it as a robust solution applicable to a diverse array of image translation tasks.</p> |
| title | Image-To-Image Translation: A Comprehensive Study on the Efficacy of Pix2Pix GAN in Producing High-Quality Visual Transformations |
| topic | Generative Adversarial Networks (GANs), Image-to-Image Translation, Conditional GANs, Unsupervised Learning, Adversarial Loss |
| url | https://doi.org/10.5281/zenodo.18280002 |