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.

本文提出了一种后续解决方案，利用在属性空间中学习的 Ising 属性先验来解决属性先验规避问题，并通过质量实验展示了它的有效性，以解决在 CelebA 和 CelebA-HQ 数据集上学习的最先进深层生成模型在仅对像素空间测量进行建模情况下可能出现的相互属性偏差的潜在空间问题。