A Systematic Study on Quantifying Bias in GAN-Augmented Data
Denis Liu
Generative adversarial networks (GANs) have recently become a popular data
augmentation technique used by machine learning practitioners. However, they
have been shown to suffer from the so-called mode collapse failure mode, which
makes them vulnerable to exacerbating biases on already skewed datasets,
resulting in the generated data distribution being less diverse than the
training distribution. To this end, we address the problem of quantifying the
extent to which mode collapse occurs. This study is a systematic effort focused
on the evaluation of state-of-the-art metrics that can potentially quantify
biases in GAN-augmented data. We show that, while several such methods are
available, there is no single metric that quantifies bias exacerbation reliably
over the span of different image domains.