  Existing GAN inversion methods work brilliantly in reconstructing
high-quality (HQ) images while struggling with more common low-quality (LQ)
inputs in practical application. To address this issue, we propose Unsupervised
Domain Adaptation (UDA) in the inversion process, namely UDA-inversion, for
effective inversion and editing of both HQ and LQ images. Regarding unpaired HQ
images as the source domain and LQ images as the unlabeled target domain, we
introduce a theoretical guarantee: loss value in the target domain is
upper-bounded by loss in the source domain and a novel discrepancy function
measuring the difference between two domains. Following that, we can only
minimize this upper bound to obtain accurate latent codes for HQ and LQ images.
Thus, constructive representations of HQ images can be spontaneously learned
and transformed into LQ images without supervision. UDA-Inversion achieves a
better PSNR of 22.14 on FFHQ dataset and performs comparably to supervised
methods.
