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。