  Generative Adversarial Networks (GANs) have been widely applied in modeling
diverse image distributions. However, despite its impressive applications, the
structure of the latent space in GANs largely remains as a black-box, leaving
its controllable generation an open problem, especially when spurious
correlations between different semantic attributes exist in the image
distributions. To address this problem, previous methods typically learn linear
directions or individual channels that control semantic attributes in the image
space. However, they often suffer from imperfect disentanglement, or are unable
to obtain multi-directional controls. In this work, in light of the above
challenges, we propose a novel approach that discovers nonlinear controls,
which enables multi-directional manipulation as well as effective
disentanglement, based on gradient information in the learned GAN latent space.
More specifically, we first learn interpolation directions by following the
gradients from classification networks trained separately on the attributes,
and then navigate the latent space by exclusively controlling channels
activated for the target attribute in the learned directions. Empirically, with
small training data, our approach is able to gain fine-grained controls over a
diverse set of bi-directional and multi-directional attributes, and we showcase
its ability to achieve disentanglement significantly better than
state-of-the-art methods both qualitatively and quantitatively.
