  Empirical works suggest that various semantics emerge in the latent space of
Generative Adversarial Networks (GANs) when being trained to generate images.
To perform real image editing, it requires an accurate mapping from the real
image to the latent space to leveraging these learned semantics, which is
important yet difficult. An in-domain GAN inversion approach is recently
proposed to constraint the inverted code within the latent space by forcing the
reconstructed image obtained from the inverted code within the real image
space. Empirically, we find that the inverted code by the in-domain GAN can
deviate from the latent space significantly. To solve this problem, we propose
a force-in-domain GAN based on the in-domain GAN, which utilizes a
discriminator to force the inverted code within the latent space. The
force-in-domain GAN can also be interpreted by a cycle-GAN with slight
modification. Extensive experiments show that our force-in-domain GAN not only
reconstructs the target image at the pixel level, but also align the inverted
code with the latent space well for semantic editing.
