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Transfer Learning via Representation Learning

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Federated and Transfer Learning

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 27))

Abstract

The remarkable performance boost of artificial intelligence (AI) algorithms is a result of re-emergence of deep neural networks that have been applied in a diverse set of applications. The success of deep learning stems from relaxing the need for the non-trivial task of feature-engineering. However, this remarkable success is conditioned on manually annotating a large amount of data points to generate suitable training datasets to supervise training of these networks. Since manual data annotation is time-consuming and expensive in many applications, learning in data-scarce regimes has been a major recent area of research focus in machine learning (ML) and AI. Transferring and reusing knowledge from a related learning problem is a core strategy for addressing challenges of learning in data-scarce regimens. Transfer learning is not a new field in ML and several great survey exist on this topic [63, 95, 98, 105, 120]. However, these existing survey are meant to be general and extensively survey many works in the area. In this chapter, we survey a very specific subset of works in this area. Our goal is to explore a framework that unifies a broad range of knowledge transfer problems as learning cross-problems relations and similarities using an representation learning. By representation learning, we mean representing the data in the input space in a latent embedding space. The latent embedding space is meant as an intermediate space to explore relationships between several ML problems. We review the recently developed algorithms that use this strategy to address several primary transfer learning settings in five primary area of: (i) online and offline multitask learning, (ii) lifelong learning and continual learning, (iii) low-shot learning, including, few-shot learning and zero-shot learning, (iv) domain adaptation, and (v) collective/distributed learning. We discuss existing challenges and future potential research directions.

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  1. University of Southern California, Los Angeles, CA, USA

    Mohammad Rostami & Muhao Chen

  2. University of Pennsylvania, Philadelphia, PA, USA

    Hangfeng He & Dan Roth

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  1. Mohammad Rostami
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Correspondence to Mohammad Rostami .

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Editors and Affiliations

  1. Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada

    Roozbeh Razavi-Far

  2. Department of Computer Science, Western University, London, ON, Canada

    Boyu Wang

  3. University of Alberta and the Alberta Machine Intelligence Institute (Amii), Edmonton, AB, Canada

    Matthew E. Taylor

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, China

    Qiang Yang

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Rostami, M., He, H., Chen, M., Roth, D. (2023). Transfer Learning via Representation Learning. In: Razavi-Far, R., Wang, B., Taylor, M.E., Yang, Q. (eds) Federated and Transfer Learning. Adaptation, Learning, and Optimization, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-11748-0_10

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