Methods and Algorithms for Unsupervised Domain Adaptation
Aug 21, 2023 - Sep 30, 2022
Even in the era of big data, many domains may still suffer from lack of high quality labeled data for machine learning. Thus, zero-shot transfer learning is particularly important, which explores methodologies to transfer models learned from a supervision-rich domain to a domain with no properly labeled training data. In this project, we will focus on zero-shot transfer learning for sophisticated models and from a supervision-rich domain to a domain whose relationship is even unknown. We will tackle several data science challenges and develop principled methods. Particularly, we will investigate cross-domain data augmentation methods, which help to generate training or testing data for an unknown domain, or transfer data from a supervision-rich domain to an unknown domain. The techniques developed in this project may be used in many important problems, such as multi-lingual natural language processing (NLP) tasks and multi-modal learning.
National Research Council Canada
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators.
Burnaby, British Columbia, CA V5A 1S6
Grants and Contributions
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Simon Fraser University
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Collaborative Science, Technology and Innovation Program – Ideation Fund
The Ideation Fund is intended to encourage, test and validate transformative research ideas generated by teams of NRC scientific personnel and external collaborators with complementary capabilities, acting as a demonstration phase for a continually-evolving suite of research and development (R&D) deliverables at the NRC. The fund supports exploratory research through two mechanisms: the New Beginnings Initiative and the Small Teams Initiative.