  StyleGAN is known to produce high-fidelity images, while also offering
unprecedented semantic editing. However, these fascinating abilities have been
demonstrated only on a limited set of datasets, which are usually structurally
aligned and well curated. In this paper, we show how StyleGAN can be adapted to
work on raw uncurated images collected from the Internet. Such image
collections impose two main challenges to StyleGAN: they contain many outlier
images, and are characterized by a multi-modal distribution. Training StyleGAN
on such raw image collections results in degraded image synthesis quality. To
meet these challenges, we proposed a StyleGAN-based self-distillation approach,
which consists of two main components: (i) A generative-based self-filtering of
the dataset to eliminate outlier images, in order to generate an adequate
training set, and (ii) Perceptual clustering of the generated images to detect
the inherent data modalities, which are then employed to improve StyleGAN's
"truncation trick" in the image synthesis process. The presented technique
enables the generation of high-quality images, while minimizing the loss in
diversity of the data. Through qualitative and quantitative evaluation, we
demonstrate the power of our approach to new challenging and diverse domains
collected from the Internet. New datasets and pre-trained models are available
at https://self-distilled-stylegan.github.io/ .
