@inbook{10.1145/3461778.3462115,
author = {Lin, David Chuan-En and Martelaro, Nikolas},
title = {Learning Personal Style from Few Examples},
year = {2021},
isbn = {9781450384766},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi-org.proxy.library.cmu.edu/10.1145/3461778.3462115},
abstract = { A key task in design work is grasping the client’s implicit tastes. Designers often do this based on a set of examples from the client. However, recognizing a common pattern among many intertwining variables such as color, texture, and layout and synthesizing them into a composite preference can be challenging. In this paper, we leverage the pattern recognition capability of computational models to aid in this task. We offer a set of principles for computationally learning personal style. The principles are manifested in PseudoClient, a deep learning framework that learns a computational model for personal graphic design style from only a handful of examples. In several experiments, we found that PseudoClient achieves a 79.40% accuracy with only five positive and negative examples, outperforming several alternative methods. Finally, we discuss how PseudoClient can be utilized as a building block to support the development of future design applications.},
booktitle = {Designing Interactive Systems Conference 2021},
pages = {1566–1578},
numpages = {13}
}