JanusGAN: GANs Disentangled Editing with Two Discriminators

Generative Adversarial Networks (GANs) have found applications in image editing. However, GANs tend to synthesize and manipulate global features, such as age, rather than focusing on local features such as facial wrinkles. Consequently, when a specific wrinkle is edited, all age-related features change as well. This paper proposes a new method that allows a GAN to learn a specific global or local disentangled edit. The method involves fine-tuning a pre-trained GAN using two discriminators, each trained on a specific dataset representing distinct states of a single disentangled feature. This approach facilitates the generator’s ability to learn features along a defined editing direction within the latent space. Importantly, to avoid interfering with prior GAN knowledge, the editing direction is defined in the meaningless dimension of the GAN latent space. Although our primary focus is on local editing, our method can be extended to global features, such as age editing. Quantitative and qualitative results show that our method provides a better balance between feature accuracy and disentanglement than other state-of-the-art methods for both local and global features. The code is available on GitHub: https://github.com/Neilstid/JanusGAN.