ImagineNet: Restyling Apps Using Neural Style Transfer
This addresses the issue of nonfunctional GUI restyling for end-users and app developers, representing an incremental improvement over existing neural style transfer methods.
The paper tackles the problem of applying neural style transfer to GUIs without making them illegible, by introducing a new loss term that retains GUI details while transferring colors and textures, resulting in all 50 evaluators preferring ImagineNet's outputs over other techniques.
This paper presents ImagineNet, a tool that uses a novel neural style transfer model to enable end-users and app developers to restyle GUIs using an image of their choice. Former neural style transfer techniques are inadequate for this application because they produce GUIs that are illegible and hence nonfunctional. We propose a neural solution by adding a new loss term to the original formulation, which minimizes the squared error in the uncentered cross-covariance of features from different levels in a CNN between the style and output images. ImagineNet retains the details of GUIs, while transferring the colors and textures of the art. We presented GUIs restyled with ImagineNet as well as other style transfer techniques to 50 evaluators and all preferred those of ImagineNet. We show how ImagineNet can be used to restyle (1) the graphical assets of an app, (2) an app with user-supplied content, and (3) an app with dynamically generated GUIs.