  Attribute prior avoidance entails subconscious or willful non-modeling of
(meta)attributes that datasets are oft born with, such as the 40 semantic
facial attributes associated with the CelebA and CelebA-HQ datasets. The
consequences of this infirmity, we discover, are especially stark in
state-of-the-art deep generative models learned on these datasets that just
model the pixel-space measurements, resulting in an inter-attribute bias-laden
latent space. This viscerally manifests itself when we perform face
manipulation experiments based on latent vector interpolations. In this paper,
we address this and propose a post-hoc solution that utilizes an Ising
attribute prior learned in the attribute space and showcase its efficacy via
qualitative experiments.
