Wasserstein Loss for Semantic Editing in the Latent Space of GANs

Perla Doubinsky (1, 2) , Nicolas Audebert (1, 2) , Michel Crucianu (1) , Hervé Le Borgne (3)

1 CEDRIC - VERTIGO - CEDRIC. Données complexes, apprentissage et représentations
2 CNAM - Conservatoire National des Arts et Métiers [CNAM]
3 CEA - Commissariat à l'énergie atomique et aux énergies alternatives

The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus allowing to modify generated images. Most supervised methods rely on the guidance of classifiers to produce such edits. However, classifiers can lead to out-of-distribution regions and be fooled by adversarial samples. We propose an alternative formulation based on the Wasserstein loss that avoids such problems, while maintaining performance on-par with classifier-based approaches. We demonstrate the effectiveness of our method on two datasets (digits and faces) using StyleGAN2.

Keywords: GAN; Wasserstein distance; image edition; GAN Wasserstein distance image edition

Domains: Artificial Intelligence [cs.AI]

Perla Doubinsky, Nicolas Audebert, Michel Crucianu, Hervé Le Borgne. Wasserstein Loss for Semantic Editing in the Latent Space of GANs. 2023. ⟨hal-04036414⟩