Improving generative adversarial network inversion via fine-tuning GAN encoders

Cheng Yu, Wenmin Wang, Roberto Bugiolacchi

Abstract: Highlights•A deep adaptive encoder is proposed to invert images into GAN latent space.•Pre-trained GAN encoders are fine-tuned to improve real image reconstruction.•Cropped images highlight main objects, improving loss function training.