TY - CHAP A1 - Kauer, Tobias A1 - Joglekar, Sagar A1 - Redi, Miriam A1 - Aiello, Luca Maria A1 - Quercia, Daniele T1 - Mapping and Visualizing Deep-Learning Urban Beautification T2 - IEEE Computer Graphics and Applications N2 - Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (goodcitylife.org/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience. KW - information visualization KW - data and knowledge visualization KW - urban informatics KW - Visualisierung KW - Deep learning KW - Computergrafik KW - Umfrage Y1 - 2018 U6 - https://doi.org/10.1109/MCG.2018.053491732 SN - 1558-1756 VL - 38 IS - 5 SP - 70 EP - 83 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York ER -