报告题目：Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
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.
We conduct experiments to compare different models on both generation and classification tasks, and show the LS-GAN is resilient against vanishing gradient and model collapse even with overtrained loss function or mildly changed network architecture.
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 respectively.