Robust Internal Representations for Domain Generalization

TL;DR

This paper surveys research on transfer learning using embedding spaces, addressing challenges like continual learning and limited labeled data, and discusses various transfer learning settings for future research directions.

Contribution

It provides a comprehensive overview of the author's research contributions in transfer learning, emphasizing the use of embedding spaces across multiple settings.

Findings

Highlights challenges in continual learning and limited data

Summarizes advances in transfer learning methods

Outlines future research directions in the field

Abstract

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.