Past Events

PhD Defense

Geometric and Spectral Limitations in Generative Adversarial Networks

 

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Monday, May 24, 2021, 10:00am - 12:00pm

 

Speaker: Mahyar Khayatkhoei

Location : Remote via Zoom

Committee

Dr. Ahmed Elgammal (advisor)

Dr. Abdeslam Boularias

Dr. Casimir Kulikowski

Dr. Dimitris Samars (outside member)

Event Type: PhD Defense

Abstract: Generative Adversarial Networks (GANs) have become one of the most successful and popular generative models in the recent years, with a wide variety of applications, such as image and audio manipulation and synthesis, style transfer, and semi-supervised learning, to name a few. The main advantage of GANs over their classical counterparts stems from the use of Deep Neural Networks (DNNs), in both the sampling process (generator) and the energy evaluation process (discriminator), which can utilize the ongoing revolution in the availability of data and computation power to effectively discover complex patterns. Yet, with this exceptional power, comes an exceptional limitation: the black-box behavior associated with DNNs, which not only places the profound promise of GANs under a shadow of mistrust, but also greatly slows down the efforts to improve the efficiency of these models. As such, studying GANs limitations and biases is critical for advancing their performance and usability in practice. The primary focus of this dissertation is to study two such limitations, namely a geometric limitation in generating disconnected manifolds, and a spectral limitation in learning distributions carried by high frequency components. We investigate these limitations both empirically and theoretically, unveil their causes and consequences, and propose and evaluate solutions for overcoming their adverse effects.

 

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