The semantically disentangled latent subspace in GAN provides rich
interpretable controls in image generation. This paper includes two
contributions on semantic latent subspace analysis in the scenario of face
generation using StyleGAN2. First, we propose a novel approach to disentangle
latent subspace semantics by exploiting existing face analysis models, e.g.,
face parsers and face landmark detectors. These models provide the flexibility
to construct various criterions with very concrete and interpretable semantic
meanings (e.g., change face shape or change skin color) to restrict latent
subspace disentanglement. Rich latent space controls unknown previously can be
discovered using the constructed criterions. Second, we propose a new
perspective to explain the behavior of a CNN classifier by generating
counterfactuals in the interpretable latent subspaces we discovered. This
explanation helps reveal whether the classifier learns semantics as intended.
Experiments on various disentanglement criterions demonstrate the effectiveness
of our approach. We believe this approach contributes to both areas of image
manipulation and counterfactual explainability of CNNs. The code is available
at https://github.com/prclibo/ice.

本文在使用 StyleGAN2 生成人脸的场景下提出了一种新方法，通过利用现有人脸分析模型（例如人脸解析器和人脸关键点检测器），对潜在子空间的语义进行解缠，并且提供了在此过程中的丰富潜在空间控制，同时提出了一种新的透视法来解释 CNN 分类器的行为，能够帮助揭示分类器所学习到的语义是否合理。

哪种风格让我更有魅力？基于可解释控制发现的 StyleGAN 因果解释

Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN

This paper describes a simple technique to analyze Generative Adversarial
Networks (GANs) and create interpretable controls for image synthesis, such as
change of viewpoint, aging, lighting, and time of day. We identify important
latent directions based on Principal Components Analysis (PCA) applied either
in latent space or feature space. Then, we show that a large number of
interpretable controls can be defined by layer-wise perturbation along the
principal directions. Moreover, we show that BigGAN can be controlled with
layer-wise inputs in a StyleGAN-like manner. We show results on different GANs
trained on various datasets, and demonstrate good qualitative matches to edit
directions found through earlier supervised approaches.

这篇论文介绍了一种简单的技术，用于分析生成对抗网络（GANs）并创建图像合成的可解释性控制，例如视角变化、衰老、照明和白天时间，通过主成分分析（PCA）在潜在空间或特征空间中应用来识别重要的潜在方向。然后，我们展示了大量可以通过沿着主要方向进行层间扰动来定义的可解释控制。此外，我们展示了 BigGAN 可以通过类似 StyleGAN 的方式用层次输入进行控制，并显示了在不同数据集上训练的各种 GAN 上的结果，并通过较早的监督方法找到了良好的定性匹配。