Conditional GANs in Image-to-Image Translation: Improving Accuracy and Contextual Relevance in Diverse Datasets

Abstract

Image-to-image translation involves converting images from one domain to another while preserving key features, making it useful for tasks like style transfer, colorization, and super-resolution. By learning the underlying relationships between different domains, this method enables the creation of realistic images that conform to the desired style. This study investigates the application of Generative Adversarial Networks (GANs) to image-to-image translation. However, experiments show that GANs often struggle to fully capture the diversity of the data, which is crucial for accurate translation between image domains. To address this limitation, the research shifts focus to Conditional Generative Adversarial Networks (CGANs), exploring their potential for overcoming the shortcomings of traditional GANs. The findings demonstrate that CGANs outperform GANs in generating high-quality images across a range of data types. By conditioning the generation process on additional input data, CGANs improve both the quality and accuracy of the generated images. This conditional approach ensures better alignment between generated outputs and input labels, leading to more consistent and precise image translations. The observed improvements highlight CGANs as a more effective model for applications requiring detailed and accurate image generation.