Generative Interpretable Visual Design: Using Disentanglement for Visual Conjoint Analysis

76 Pages Posted: 13 Jul 2022 Last revised: 11 Oct 2023

See all articles by Ankit Sisodia

Ankit Sisodia

Mitchell E. Daniels, Jr School of Business, Purdue University; Yale School of Management

Alex Burnap

Yale School of Management

Vineet Kumar

Yale School of Management

Date Written: October 15, 2023

Abstract

This paper develops a method to automatically discover and quantify human-interpretable visual characteristics directly from image data. Our method is generative, and creates new ``ideal point" visual designs for targeted consumer segments. The approach developed here builds on ``disentanglement" methods in deep learning using variational autoencoders, which aim to discover underlying statistically independent and interpretable characteristics constituting an object's visual representation. While the deep learning literature shows that supervision with ``ground truth" characteristics is required to obtain unique disentangled representations, these are typically unknown in real world applications and are in fact exactly the characteristics we aim to discover. Our method instead uses readily available structured product characteristics as supervisory signals to enable disentanglement, and discovers and quantifies human interpretable and statistically independent characteristics without domain knowledge on the product category. The approach is used with a dataset on watches to automatically discover interpretable visual product characteristics, obtain consumer preferences over these characteristics, and generate new ``ideal point" visual product designs targeted to specific consumer segments.

Keywords: discovery of product characteristics, deep learning, disentanglement

Suggested Citation

Sisodia, Ankit and Burnap, Alex and Kumar, Vineet, Generative Interpretable Visual Design: Using Disentanglement for Visual Conjoint Analysis (October 15, 2023). Available at SSRN: https://ssrn.com/abstract=4151019 or http://dx.doi.org/10.2139/ssrn.4151019

Ankit Sisodia (Contact Author)

Mitchell E. Daniels, Jr School of Business, Purdue University ( email )

403 Mitch Daniels Blvd.
West Lafayette, IN 47907
United States

Yale School of Management

Alex Burnap

Yale School of Management ( email )

165 Whitney Avenue
New Haven, CT 06511
United States

Vineet Kumar

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

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