1 What is transfer learning?


This chapter covers

  • What exactly transfer learning is, both generally in artificial intelligence (AI) and in the context of natural language processing (NLP)
  • Typical NLP tasks and the related chronology of NLP transfer learning advances
  • An overview of transfer learning in computer vision
  • The reason for the recent popularity of NLP transfer learning techniques

Artificial intelligence (AI) has transformed modern society in a dramatic way. Machines now perform tasks that human used to do, and they do them faster, cheaper, and, in some cases, more effectively. Popular examples include computer vision applications, which teach computers how to understand images and videos, such as for the detection of criminals in closed-circuit television camera feeds. Other computer vision applications include the detection of diseases from images of patients’ organs and the defining of plant species from plant leaves. Another important branch of AI, natural language processing (NLP), deals particularly with the analysis and processing of human natural language data. Examples of NLP applications include speech-to-text transcription and translation between various languages.

1.1 Overview of representative NLP tasks

1.2 Understanding NLP in the context of AI

1.2.1 Artificial intelligence (AI)

1.2.2 Machine learning

1.2.3 Natural language processing (NLP)

1.3 A brief history of NLP advances

1.3.1 General overview

1.3.2 Recent transfer learning advances

1.4 Transfer learning in computer vision

1.4.1 General overview

1.4.2 Pretrained ImageNet models

1.4.3 Fine-tuning pretrained ImageNet models

1.5 Why is NLP transfer learning an exciting topic to study now?