Authors:
Walid Messaoud
1
;
Rim Trabelsi
2
;
Adnane Cabani
3
and
Fatma Abdelkefi
1
Affiliations:
1
Supcom Lab-MEDIATRON, Carthage University, Ariana, Tunisia
;
2
Hatem Bettaher IResCoMath Laboratory, National Engineering School, University of Gabes, Tunisia
;
3
Université de Rouen Normandie, ESIGELEC, IRSEEM, 76000 Rouen, France
Keyword(s):
Generative Adversarial Networks, Latent Space Manipulation, Conjugate Gradient, StyleGAN, Neural Network.
Abstract:
Generative Adversarial Networks (GANs) have revolutionized image generation, allowing the production of high-quality images from latent codes in the latent space. However, manipulating the latent space to achieve specific image attributes remains challenging. Existing methods often lack disentanglement, leading to unintended changes in other attributes. Moreover, most of the existing techniques are limited to one-dimensional conditioning, making them less effective for complex multidimensional modifications. In this paper, we propose a novel approach that combines an auxiliary map composed of convolutional layers and Conjugate Gradient (CG) to enhance latent space manipulation. The proposed auxiliary map provides a versatile and expressive way to incorporate external information for image generation, while CG facilitates precise and controlled manipulations. Our experimental results demonstrate better performance compared to state-of-the-art methods.