Neural Style Transfer, Creating Art with Deep Learning

Neural style transfer is a deep learning technique that is the topic of study in this research paper. In NST two images are combined, a content and a style image. Where, while retaining the content image the artistic style of style image is imposed on the content image. In this research, a series of ways are explored to improve the efficiency and visual quality of the generated image. This research is about improvising and molding the existing losses, to improve the existing methodologies. Key contributions are dynamic weighting of content and style losses, multi scale loss computation for preserving the details in a better way. This kind of loss improvisation and changing is being used, so that not only the high-level structures and fine details are maintained in the image generated. These dynamic and multiple kind of losses will be implemented to retain the essence of the content in the content image. While generating a new image the content or style is overcompensated and most of the time one of these is having a stronger effect on the final image in most of the techniques, our motive is to eradicate the same and generate an excellent image. Along with this, various optimization techniques are studied in the underlying paper to compensate this computation cost of the newly introduced losses. The ways provide a framework for a neural style transfer with a higher quality and efficient combining of images.