  We explore different design choices for injecting noise into generative
adversarial networks (GANs) with the goal of disentangling the latent space.
Instead of traditional approaches, we propose feeding multiple noise codes
through separate fully-connected layers respectively. The aim is restricting
the influence of each noise code to specific parts of the generated image. We
show that disentanglement in the first layer of the generator network leads to
disentanglement in the generated image. Through a grid-based structure, we
achieve several aspects of disentanglement without complicating the network
architecture and without requiring labels. We achieve spatial disentanglement,
scale-space disentanglement, and disentanglement of the foreground object from
the background style allowing fine-grained control over the generated images.
Examples include changing facial expressions in face images, changing beak
length in bird images, and changing car dimensions in car images. This
empirically leads to better disentanglement scores than state-of-the-art
methods on the FFHQ dataset.
