New approaches for heterogeneous transfer learning
Tianyi Zhou · 2015
In many real-world problems, it is often time-consuming and expensive to collect labeled data.To alleviate this challenge, transfer learning (TL) techniques that adapt a model from a related task with ample labeled data to a task of interest with little or no additional human supervision have been proposed in recent years.Most TL methods assume that First and foremost, I would like to express my deepest gratitude to my academic supervisor, Dr. Ho Shen-shyang and my previous supervisor Dr. Ivor Wai-Hung Tsang, for patiently giving guidance and encouragement through my Ph.D research.Without their consistent support and supervision, this research would not have been possible.