About Rameen Abdal Rameen Abdal Ph.D. Student, Computer Science machine learning Computer Vision Deep learning Rameen Abdal is a Master student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). He has graduated in 2023. Education and Early Career Rameen Abdal obtained his bachelor of technology degree in Electronics and Communications Engineering from the National Institute of Technology in Srinagar, India in 2018. After that, he joined KAUST for the MS/PhD program in computer science to continue his studies. Before joining KAUST, Rameen was an intern at Texas Instruments (TI) Center for Embedded Product Design at Netaji Subhas University of Events Presented Events Feb 12 - Feb 18, 2023 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 GaN 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.
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 GaN 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.
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