Learning Personal Style from Few Examples

TL;DR

This paper introduces PseudoClient, a deep learning framework that learns personal graphic design styles from very few examples, aiding designers in capturing client preferences more efficiently.

Contribution

The paper presents a novel deep learning approach that effectively learns personal design styles from limited examples, outperforming existing methods.

Findings

Achieves 79.40% accuracy with five examples

Outperforms several alternative methods

Demonstrates potential for supporting future design tools

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.