Analysis of Hyperparameter Tuning in Neural Style Transfer

Most of the notable artworks of all time are hand drawn by great artists. But, now with the advancement in image processing and huge computation power, very sophisticated synthesised artworks are being produced. Since mid-1990's, computer graphics engineers have come up with algorithms to produce digital paintings, but the results were not visually appealing. Recently, neural networks have been used to do this task and the results seen are like never before. One such algorithm for this purpose is the neural style transfer algorithm, which imparts the pattern from one image to another, producing marvellous pieces of art. This research paper focuses on the roles of various parameters involved in the neural style transfer algorithm. An extensive analysis of how these parameters influence the output, in terms of time, performance and quality of the style transferred image produced is also shown in the paper. A concrete comparison has been drawn on the basis of different time and performance metrics. Finally, optimal values for these discussed parameters have been suggested.