GANs and Its Beyond

Abstract:

Generative adversarial networks (GANs) involve two neural networks, pitting one against the other. GANs were called as the most interesting idea in the last 10 years in machine learning by Turing award recipient Yann LeCun. However, beyond playing with the classical image generation task in plain GANs, many different techniques and applications have been introduced to fulfill the potential of adversarial thoughts over the last few years. In this talk, we will begin with the introduction of plain GANs, and then illustrate various ways of exploring and exploiting adversarial training in different machine learning and computer vision problems.

Bio:

Chang Xu is Senior Lecturer and ARC DECRA Fellow at the School of Computer Science, University of Sydney. He received the Ph.D. degree from Peking University, China. His research finterests lie in machine learning algorithms and related applications in computer vision. He has published over 100 papers in prestigious journals and top tier conferences, including IEEE T-PAMI, IEEE T-NNLS, IEEE T-IP, ICML, NIPS, IJCAI and AAAI. He has received several paper awards, including Distinguished Paper Award in IJCAI 2018. He regularly severed as the PC member or Senior PC member for many conferences, e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, IJCAI and AAAI. He has been recognized as Top Ten Distinguished Senior PC in IJCAI 2017.

会议主题：徐畅教授学术报告

会议时间：2021/04/14 10:00-11:00 (GMT+08:00) 中国标准时间 - 北京

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会议 ID：103 326 393