Generative Adversarial Networks (GANs) have witnessed significant advances in
recent years, generating increasingly higher quality images, which are
non-distinguishable from real ones. Recent GANs have proven to encode features
in a disentangled latent space, enabling precise control over various semantic
attributes of the generated facial images such as pose, illumination, or
gender. GAN inversion, which is projecting images into the latent space of a
GAN, opens the door for the manipulation of facial semantics of real face
images. This is useful for numerous applications such as evaluating the
performance of face recognition systems. In this work, EGAIN, an architecture
for constructing GAN inversion models, is presented. This architecture
explicitly addresses some of the shortcomings in previous GAN inversion models.
A specific model with the same name, egain, based on this architecture is also
proposed, demonstrating superior reconstruction quality over state-of-the-art
models, and illustrating the validity of the EGAIN architecture.

基于 EGAIN 架构的 GAN 逆向模型能够重构出真实面部图像的语义结构，具有优于现有模型的重建质量。

EGAIN: 扩展的 GAN 逆向

EGAIN: Extended GAn INversion

We propose a method to disentangle linear-encoded facial semantics from
StyleGAN without external supervision. The method derives from linear
regression and sparse representation learning concepts to make the disentangled
latent representations easily interpreted as well. We start by coupling
StyleGAN with a stabilized 3D deformable facial reconstruction method to
decompose single-view GAN generations into multiple semantics. Latent
representations are then extracted to capture interpretable facial semantics.
In this work, we make it possible to get rid of labels for disentangling
meaningful facial semantics. Also, we demonstrate that the guided extrapolation
along the disentangled representations can help with data augmentation, which
sheds light on handling unbalanced data. Finally, we provide an analysis of our
learned localized facial representations and illustrate that the semantic
information is encoded, which surprisingly complies with human intuition. The
overall unsupervised design brings more flexibility to representation learning
in the wild.

本文介绍了一种无需外部监督即可将线性编码的面部语义从 StyleGAN 中分离的方法，并结合稳定的三维可变形面部重建方法将单视图 GAN 生成物分解为多个语义。同时，我们还展示了沿着分离表示进行引导外推可以帮助进行数据增强，并提供了对我们学习到的面部表示的分析。

线性编码面部语义的无监督解缠

Unsupervised Disentanglement of Linear-Encoded Facial Semantics

This work showcases a new approach for causal discovery by leveraging user
experiments and recent advances in photo-realistic image editing, demonstrating
a potential of identifying causal factors and understanding complex systems
counterfactually. We introduce the beauty learning problem as an example, which
has been discussed metaphysically for centuries and been proved exists, is
quantifiable, and can be learned by deep models in our recent paper, where we
utilize a natural image generator coupled with user studies to infer causal
effects from facial semantics to beauty outcomes, the results of which also
align with existing empirical studies. We expect the proposed framework for a
broader application in causal inference.

本研究利用用户实验和照片逼真的图像编辑技术展示了一种新的因果推断方法，展示了识别因果因素和理解复杂系统的潜力，并以美学学习问题为例进行讨论，该问题在过去几个世纪中一直在形而上学上讨论，我们最近的论文证明了它的存在，并且可以被深度模型学习，通过自然图像生成器和用户研究推断出从面部语义到美学结果的因果效应，这与现有的经验研究结果相一致。我们预计该框架在因果推断的更广泛应用方面有应用潜力。