Tabular Data to Image Generation: Benchmark Data, Approaches, and Evaluation

Alex Tang, Gromit Yeuk-Yin Chan, Ryan A. Rossi, Chang Xiao, Eunyee Koh

Abstract:

In this work, we study the problem of generating a set of images from an arbitrary tabular dataset. The set of generated images provides an intuitive visual summary of the tabular data that can be quickly and easily communicated and understood by the user.
More specifically, we formally introduce this new dataset to image generation task and discuss a few motivating applications including exploratory data analysis and understanding customer segments for creating better marketing campaigns.
We then curate a benchmark dataset for training such models, which we release publicly for others to use and develop new models for other important applications of interest.
Further, we describe a general and flexible framework that serves as a fundamental basis for studying and developing models for this new task of generating images from tabular data.
From the framework, we propose a few different approaches with varying levels of complexity and tradeoffs.
One such approach leverages both numerical and textual data as the input to our image generation pipeline.
The pipeline consists of an image decoder and a conditional auto-regressive sequence generation model which also includes a pre-trained tabular representation in the input layer.
We evaluate the performance of these approaches through several quantitative metrics (FID for image quality and LPIPS scores for image diversity).

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TL;DR: We study the problem of generating a set of images from an arbitrary tabular dataset