Revolutionizing comic coloring: Federated learning-based neural network for efficiency and privacy

Abstract

The conventional method of coloring comics can be quite arduous and time-consuming, particularly within the realm of animation, where each frame necessitates individual coloring. Over the past few years, the advent of deep learning technology has introduced fresh prospects for comic coloring. Nonetheless, it's worth noting that deep learning typically demands a substantial volume of data to effectively train models, giving rise to legitimate concerns regarding data privacy. This study proposes an innovative approach that applies federated learning to comic coloring to improve efficiency and address privacy issues. In this research, an existing neural network European Conference on Computer Vision (ECCV16) model was employed using federated learning to partition and train the model on segmented databases. To better quantify the color differences between the original images and the colored images, this study designed a custom loss function. Experimental results demonstrate that this approach has been effective in comic coloring, demonstrating the feasibility of applying federated learning to the task of comic coloring.