Robust GAN inversion
Egor Sevriugov Ivan Oseledets
Recent advancements in real image editing have been attributed to the
exploration of Generative Adversarial Networks (GANs) latent space. However,
the main challenge of this procedure is GAN inversion, which aims to map the
image to the latent space accurately. Existing methods that work on extended
latent space $W+$ are unable to achieve low distortion and high editability
simultaneously. To address this issue, we propose an approach which works in
native latent space $W$ and tunes the generator network to restore missing
image details. We introduce a novel regularization strategy with learnable
coefficients obtained by training randomized StyleGAN 2 model - WRanGAN. This
method outperforms traditional approaches in terms of reconstruction quality
and computational efficiency, achieving the lowest distortion with 4 times
fewer parameters. Furthermore, we observe a slight improvement in the quality
of constructing hyperplanes corresponding to binary image attributes. We
demonstrate the effectiveness of our approach on two complex datasets:
Flickr-Faces-HQ and LSUN Church.