Exploring Semantic Variations in GAN Latent Spaces via Matrix
  Factorization
Andrey Palaev Rustam A. Lukmanov Adil Khan
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.