StyleDiff: Attribute Comparison Between Unlabeled Datasets in Latent Disentangled Space
One major challenge in machine learning applications is coping with
mismatches between the datasets used in the development and those obtained in
real-world applications. These mismatches may lead to inaccurate predictions
and errors, resulting in poor product quality and unreliable systems. In this
study, we propose StyleDiff to inform developers of the differences between the
two datasets for the steady development of machine learning systems. Using
disentangled image spaces obtained from recently proposed generative models,
StyleDiff compares the two datasets by focusing on attributes in the images and
provides an easy-to-understand analysis of the differences between the
datasets. The proposed StyleDiff performs in $O (d N\log N)$, where $N$ is the
size of the datasets and $d$ is the number of attributes, enabling the
application to large datasets. We demonstrate that StyleDiff accurately detects
differences between datasets and presents them in an understandable format
using, for example, driving scenes datasets.