Recent advances in generative adversarial networks have shown that it is
possible to generate high-resolution and hyperrealistic images. However, the
images produced by GANs are only as fair and representative as the datasets on
which they are trained. In this paper, we propose a method for directly
modifying a pre-trained StyleGAN2 model that can be used to generate a balanced
set of images with respect to one (e.g., eyeglasses) or more attributes (e.g.,
gender and eyeglasses). Our method takes advantage of the style space of the
StyleGAN2 model to perform disentangled control of the target attributes to be
debiased. Our method does not require training additional models and directly
debiases the GAN model, paving the way for its use in various downstream
applications. Our experiments show that our method successfully debiases the
GAN model within a few minutes without compromising the quality of the
generated images. To promote fair generative models, we share the code and
debiased models at this http URL

本文提出了一种直接修改预训练 StyleGAN2 模型的方法，该方法利用 StyleGAN2 模型的样式空间对目标属性进行解缠，从而生成相对公平的图像集合。实验表明，我们的方法可以在几分钟内成功地消除 GAN 模型的偏见，而不会影响所生成的图像的质量，以此来推进公平生成模型的发展。

FairStyle: 使用样式通道操控消除 StyleGAN2 偏见

FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations

We propose a framework based on Generative Adversarial Networks to
disentangle the identity and attributes of faces, such that we can conveniently
recombine different identities and attributes for identity preserving face
synthesis in open domains. Previous identity preserving face synthesis
processes are largely confined to synthesizing faces with known identities that
are already in the training dataset. To synthesize a face with identity outside
the training dataset, our framework requires one input image of that subject to
produce an identity vector, and any other input face image to extract an
attribute vector capturing, e.g., pose, emotion, illumination, and even the
background. We then recombine the identity vector and the attribute vector to
synthesize a new face of the subject with the extracted attribute. Our proposed
framework does not need to annotate the attributes of faces in any way. It is
trained with an asymmetric loss function to better preserve the identity and
stabilize the training process. It can also effectively leverage large amounts
of unlabeled training face images to further improve the fidelity of the
synthesized faces for subjects that are not presented in the labeled training
face dataset. Our experiments demonstrate the efficacy of the proposed
framework. We also present its usage in a much broader set of applications
including face frontalization, face attribute morphing, and face adversarial
example detection.

提出了一种基于生成对抗网络 (GAN) 的框架，以在开放领域中保留身份的人脸合成中，区分脸部的身份和属性。它可以方便地重新组合不同身份和属性，以合成身份保持的面部。