GaN

Extracting Semantic and Geometric Information in Images and Videos using GANs

Rameen Abdal, Ph.D. Student, Computer Science

Feb 15, 18:00 - 20:00

B1 L2 R2202

The success of Generative Adversarial Networks (GANs) has resulted in unprecedented quality both for image generation and manipulation. Recent state-of-the-art GANs (e.g., the StyleGAN series) have demonstrated outstanding results in photo-realistic image generation. In this dissertation, we explore the latent space properties, including image manipulation, extraction of 3D properties, and performing various weakly supervised and unsupervised downstream tasks using StyleGAN and its derivative architectures.