6月15日：Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Lecture Series in Intelligent Perception and Computing
题 目（TITLE）：Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
讲 座 人（SPEAKER）: Dr. Guojun Qi; University of Central Florida
主持人 (CHAIR): Prof. Ran He
时 间 (TIME)：JUNE 15, 2017 (Thursday), 10:00AM
地 点 (VENUE)：Meeting Room No.1, 3 Floor, Intelligent Building
This talk will present a novel Loss-Sensitive GAN (LS-GAN) that learns a loss function to separate generated samples from their real examples. The theoretical analysis shows that LS-GAN can generate samples following the true data density. In particular, we present a regularity condition on the underlying data density, which allows us to use a class of Lipschitz losses and generators to model LS-GAN. It relaxes the assumption that the classic GAN should have infinite modeling capacity to obtain the similar theoretical guarantee. We also derive a non-parametric solution that characterizes the upper and lower bounds of the losses learned by LS-GAN, both of which are cone-shaped and have non-vanishing gradient almost everywhere. This shows there will be sufficient gradient to update the generator of the LS-GAN even if the loss function is over-trained, relieving the vanishing gradient problem in the classic GAN. We also extend the unsupervised LS-GAN to a conditional model generating samples based on given conditions, and show its applications in both supervised and semi-supervised learning problems.
Dr. Qi is an assistant professor of Computer Science in the University of Central Florida. Prior to joining UCF, he was a Research Staff Member at the IBM T.J. Watson Research Center (Yorktown Heights, NY). He worked with Professor Thomas Huang in the Image Formation and Processing Group at the Beckman Institute in the University of Illinois at Urbana-Champaign, and received the Ph.D. in Electrical and Computer Engineering in December 2013. His main research interests include computer vision, pattern recognition, data mining, and multimedia computing. In particular, he is interested in information and knowledge discovery, analysis and aggregation, from multiple data sources of diverse modalities (e.g., images, audios, sensors and text). His research also aims at effectively leveraging and aggregating data shared in an open connected environment (e.g., social, sensor and mobile networks), as well as developing computational models and theory for general-purpose knowledge and information systems.
Dr. Qi's researches have been published in several venues, including CVPR, ICCV, ACM Multimedia, KDD, ICML, IEEE T. PAMI, IEEE T. KDE, Proceedings of IEEE. He has served or will serve as Area Chair (Senior Program Committee Member) for ICCV, ACM Multimedia, KDD, CIKM and ICME. He also was a Program Committee Chair for MMM 2016. In addition, he has co-edited two special issues of "Deep Learning for Multimedia Computing" and "Big Media Data: Understanding, Search and Mining" for IEEE T. Multimedia and IEEE T. Big Data respecitvely.