  Controlled data generation with GANs is desirable but challenging due to the
nonlinearity and high dimensionality of their latent spaces. In this work, we
explore image manipulations learned by GANSpace, a state-of-the-art method
based on PCA. Through quantitative and qualitative assessments we show: (a)
GANSpace produces a wide range of high-quality image manipulations, but they
can be highly entangled, limiting potential use cases; (b) Replacing PCA with
ICA improves the quality and disentanglement of manipulations; (c) The quality
of the generated images can be sensitive to the size of GANs, but regardless of
their complexity, fundamental controlling directions can be observed in their
latent spaces.
