VALSE Webinar 22-16-283 Contrastive Representive Learning

Image credit: Image


Mingming Gong is a lecturer and PhD supervisor at the School of Mathematics and Statistics, University of Melbourne, Australia, and a principal investigator at the Melbourne Centre for Data Science. He received his PhD from the University of Technology Sydney in 2017 and then did postdoctoral research at the University of Pittsburgh and Carnegie Mellon University. His research interests include causal machine learning, weakly supervised/ self-supervised learning, transfer learning, generative models, and 3D vision. He has published more than 50 papers in top conferences and journals related to artificial intelligence, such as NeurIPS, ICML, and CVPR. He is a recipient of the Australian Research Council Discovery Early Career Award in 2021. He is area chairs of top machine learning conferences such as NeurIPS, ICML, and ICLR.

Jun 29, 2022 6:00 PM — 8:00 PM
  1. 对比学习的成功涉及很多技术细节,包括数据增强,负样本,动量编码器,projection head等等,如何可以化繁为简,并取得不错的效果?
  2. 目前对比学习多用于image-level分类任务,未来会不会更多的应用在pixel-level的下游任务?可能带来哪些优势?
  3. 对比学习可以使用labels吗?与metric learning相比,主要区别在哪里?
  4. 好的对比学习系统应该满足什么条件呢?或者说如何更好的评价对比学习的效果?
  5. 未来几年,对比学习和其他方法的结合,有哪些发展趋势?