The Community for Technology Leaders
Green Image
Issue No. 05 - Sep./Oct. (2018 vol. 38)
ISSN: 0272-1716
pp: 70-83
Tobias Kauer , University of Applied Sciences Potsdam
Sagar Joglekar , Kings College London
Miriam Redi , Nokia Bell Labs Cambridge
Luca Maria Aiello , Nokia Bell Labs Cambridge
Daniele Quercia , Nokia Bell Labs Cambridge
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 ( 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.
data visualisation, learning (artificial intelligence)

T. Kauer, S. Joglekar, M. Redi, L. M. Aiello and D. Quercia, "Mapping and Visualizing Deep-Learning Urban Beautification," in IEEE Computer Graphics and Applications, vol. 38, no. 5, pp. 70-83, 2018.
257 ms
(Ver 3.3 (11022016))