  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 \url{https://github.com/prclibo/ice}.
