generative adversarial network

Stylistic and spatial disentanglement in GANs

Yazeed Alharbi, Ph.D., Computer Science

Aug 12, 14:00 - 16:00

KAUST

Computer Vision machine learning generative adversarial network Deep learning

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

Yazeed Alharbi

Ph.D., Computer Science

Computer Vision machine learning Deep learning generative adversarial network convolutional neural network

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused