  Various controls over the generated data can be extracted from the latent
space of a pre-trained GAN, as it implicitly encodes the semantics of the
training data. The discovered controls allow to vary semantic attributes in the
generated images but usually lead to entangled edits that affect multiple
attributes at the same time. Supervised approaches typically sample and
annotate a collection of latent codes, then train classifiers in the latent
space to identify the controls. Since the data generated by GANs reflects the
biases of the original dataset, so do the resulting semantic controls. We
propose to address disentanglement by subsampling the generated data to remove
over-represented co-occuring attributes thus balancing the semantics of the
dataset before training the classifiers. We demonstrate the effectiveness of
this approach by extracting disentangled linear directions for face
manipulation on two popular GAN architectures, PGGAN and StyleGAN, and two
datasets, CelebAHQ and FFHQ. We show that this approach outperforms
state-of-the-art classifier-based methods while avoiding the need for
disentanglement-enforcing post-processing.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition