Style Transfer Model

Style transfer models aim to transform the style of one data modality (e.g., image, audio, text) while preserving its content, mimicking the style of another example. Recent research focuses on improving controllability and efficiency, exploring architectures like transformers and diffusion models, and addressing challenges such as disentangling content and style features, handling limited or non-parallel data, and ensuring content preservation. These advancements have implications for various applications, including image editing, voice conversion, text generation, and bias mitigation in natural language processing, offering tools for creative expression and data manipulation.