chapter one

1 What is Transfer Learning?

 

This chapter covers:

  • What exactly Transfer Learning is, both generally in AI and in the context of Natural Language Processing (NLP)
  • Typical NLP tasks, and related chronology of NLP Transfer Learning advances
  • Overview of Transfer Learning in Computer Vision
  • Why the recent popularity of NLP Transfer Learning techniques, and whether it can be expected to last

Artificial Intelligence (AI) has transformed modern society in a dramatic way. Tasks which were previously done by humans can now be done by machines faster, cheaper and in some cases more effectively. Popular examples of this include computer vision applications – concerned with teaching computers how to understand images and videos – for the detection of criminals in closed-circuit television camera feeds, for instance. Other computer vision applications include detection of various diseases from images of plant leaves, and detection of plant species from plant leaves. Another important branch of AI, which deals particularly with automating the analysis and processing of human natural language data, is referred to as Natural Language Processing (NLP). Examples of NLP applications include speech-to-text transcription, translation between various languages, among many others.

1.1           Overview of Representative NLP Tasks

1.2           Understanding Natural Language Processing (NLP) in the Context of Artificial Intelligence (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  Pre-trained ImageNet Models

1.4.3  Fine-tuning Pre-trained ImageNet Models

1.5           Why is NLP Transfer Learning an Exciting Topic to Study Now?

1.6           Summary