  StyleGAN is able to produce photorealistic images that are almost
indistinguishable from real photos. The reverse problem of finding an embedding
for a given image poses a challenge. Embeddings that reconstruct an image well
are not always robust to editing operations. In this paper, we address the
problem of finding an embedding that both reconstructs images and also supports
image editing tasks. First, we introduce a new normalized space to analyze the
diversity and the quality of the reconstructed latent codes. This space can
help answer the question of where good latent codes are located in latent
space. Second, we propose an improved embedding algorithm using a novel
regularization method based on our analysis. Finally, we analyze the quality of
different embedding algorithms. We compare our results with the current
state-of-the-art methods and achieve a better trade-off between reconstruction
quality and editing quality.
