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Attribute prior avoidance entails subconscious or willful non-modeling of(meta)attributes that datasets are oft born with, such as the 40 semanticfacial attributes associated with the CelebA and CelebA-HQ datasets. Theconsequences of this infirmity, we discover, are especially stark instate-of-the-art deep generative models learned on these datasets that justmodel the pixel-space measurements, resulting in an inter-attribute bias-ladenlatent space. This viscerally manifests itself when we perform facemanipulation experiments based on latent vector interpolations. In this paper,we address this and propose a post-hoc solution that utilizes an Isingattribute prior learned in the attribute space and showcase its efficacy viaqualitative experiments.

Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance