Learning Personal Style from Few Examples

by   David Chuan-En Lin, et al.

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 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.



page 6

page 9


Augmenting C. elegans Microscopic Dataset for Accelerated Pattern Recognition

The detection of cell shape changes in 3D time-lapse images of complex t...

Redycler: Daily Outfit Texture Fabrication Appliance Using Re-Programmable Dyes

We present a speculative design for a novel appliance for future fabrica...

Inbetween: Visual Selection in Parametric Design

The act of selection plays a leading role in the design process and in t...

Reproducible Pattern Recognition Research: The Case of Optimistic SSL

In this paper, we discuss the approaches we took and trade-offs involved...

Hints and Principles for Computer System Design

This new long version of my 1983 paper suggests the goals you might have...

Algorithmic support of a personal virtual assistant for automating the processing of client requests

This article describes creating algorithmic support for the functioning ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.