Robust Internal Representations for Domain Generalization
Mohammad Rostami
This paper which is part of the New Faculty Highlights Invited Speaker
Program of AAAI'23, serves as a comprehensive survey of my research in transfer
learning by utilizing embedding spaces. The work reviewed in this paper
specifically revolves around the inherent challenges associated with continual
learning and limited availability of labeled data. By providing an overview of
my past and ongoing contributions, this paper aims to present a holistic
understanding of my research, paving the way for future explorations and
advancements in the field. My research delves into the various settings of
transfer learning, including, few-shot learning, zero-shot learning, continual
learning, domain adaptation, and distributed learning. I hope this survey
provides a forward-looking perspective for researchers who would like to focus
on similar research directions.