  StyleGAN has shown strong potential for disentangled semantic control, thanks
to its special design of multi-layer intermediate latent variables. However,
existing semantic discovery methods on StyleGAN rely on manual selection of
modified latent layers to obtain satisfactory manipulation results, which is
tedious and demanding. In this paper, we propose a model that automates this
process and achieves state-of-the-art semantic discovery performance. The model
consists of an attention-equipped navigator module and losses contrasting
deep-feature changes. We propose two model variants, with one contrasting
samples in a binary manner, and another one contrasting samples with learned
prototype variation patterns. The proposed losses are defined with pretrained
deep features, based on our assumption that the features can implicitly reveal
the desired semantic structure including consistency and orthogonality.
Additionally, we design two metrics to quantitatively evaluate the performance
of semantic discovery methods on FFHQ dataset, and also show that disentangled
representations can be derived via a simple training process. Experimentally,
our models can obtain state-of-the-art semantic discovery results without
relying on latent layer-wise manual selection, and these discovered semantics
can be used to manipulate real-world images.
