The goal of attribute manipulation is to control specified attribute(s) in
given images. Prior work approaches this problem by learning disentangled
representations for each attribute that enables it to manipulate the encoded
source attributes to the target attributes. However, encoded attributes are
often correlated with relevant image content. Thus, the source attribute
information can often be hidden in the disentangled features, leading to
unwanted image editing effects. In this paper, we propose an Attribute
Information Removal and Reconstruction (AIRR) network that prevents such
information hiding by learning how to remove the attribute information
entirely, creating attribute excluded features, and then learns to directly
inject the desired attributes in a reconstructed image. We evaluate our
approach on four diverse datasets with a variety of attributes including
DeepFashion Synthesis, DeepFashion Fine-grained Attribute, CelebA and
CelebA-HQ, where our model improves attribute manipulation accuracy and top-k
retrieval rate by 10% on average over prior work. A user study also reports
that AIRR manipulated images are preferred over prior work in up to 76% of
cases.

本文提出了一种 Attribute Information Removal and Reconstruction（AIRR）网络，通过学习如何完全去除属性信息创建不包含属性信息的特征，然后学习在重建图像中直接注入所需属性，以避免信息隐藏问题，并在四个数据集上评估。结果表明，与先前的工作相比，我们的模型平均提高了 10％的属性操作准确性和 top-k 检索率，用户研究也表明 AIRR 操作的图像在高达 76％的情况下优于先前的工作。