  Generative Adversarial Networks (GANs) are susceptible to bias, learned from
either the unbalanced data, or through mode collapse. The networks focus on the
core of the data distribution, leaving the tails - or the edges of the
distribution - behind. We argue that this bias is responsible not only for
fairness concerns, but that it plays a key role in the collapse of
latent-traversal editing methods when deviating away from the distribution's
core. Building on this observation, we outline a method for mitigating
generative bias through a self-conditioning process, where distances in the
latent-space of a pre-trained generator are used to provide initial labels for
the data. By fine-tuning the generator on a re-sampled distribution drawn from
these self-labeled data, we force the generator to better contend with rare
semantic attributes and enable more realistic generation of these properties.
We compare our models to a wide range of latent editing methods, and show that
by alleviating the bias they achieve finer semantic control and better identity
preservation through a wider range of transformations. Our code and models will
be available at https://github.com/yzliu567/sc-gan
