Contrastive Learning in Image Style Transfer: A Thorough Examination using CAST and UCAST Frameworks

In the domain of image processing and manipulation, image style transfer has emerged as a revolutionary technique that allows the fusion of artistic styles onto photographic content. This technology has been leveraged in recent years in fields as varied as content creation, fashion design and augmented reality among others. Despite significant advancements, conventional style transfer methods often struggle with preserving the content of the input image while accurately infusing the desired style. Significant computational overheads are also often incurred during the execution of most existing style transfer frameworks. In this paper, we examine the CAST and UCAST frameworks that rely on a contrastive learning mechanism as a possible solution to the aforementioned challenges. This method eschews the use of second-order statistics such as the Gram matrix for content features in favor of comparing the features of two images side by side and extracting information based on their stylistic similarities and differences. We provide a high-level overview of the system architecture and briefly discuss the results of an experimental implementation of the framework.