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Transfer learning is one of the most important technologies in the era of Artificial Intelligence and Deep Learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning.
This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
Machine Learning is a kind of important learning methodology of Artificial Intelligence, which has gained great proliferation in the past decades. Machine Learning makes it possible to learn knowledge from the data. Transfer learning, as an important branch of Machine Learning, focuses on the process of leveraging the learned knowledge to facilitate the learning of new ability, which increases the effectiveness and efficiency.
Concretely speaking, in the field of machine learning, transfer learning can be generally defined as (informal): Transfer learning aims to solve the new problem by leveraging the similarity of data (task or models) between the old problem and the new one to perform knowledge (experience, rules, etc.) transfer.
Foundations
Introduction
From Machine Learning to Transfer Learning
Overview of Transfer Learning Algorithms
Instance Weighting Methods
Statistical Feature Transformation Methods
Geometrical Feature Transformation Methods
Theory, Evaluation, and Model Selection
Modern Transfer Learning
Pre-Training and Fine-Tuning
Deep Transfer Learning
Adversarial Transfer Learning
Generalization in Transfer Learning
Safe and Robust Transfer Learning
Transfer Learning in Complex Environments
Low-Resource Learning
Applications of Transfer Learning
Transfer Learning for Computer Vision
Transfer Learning for Natural Language Processing
Transfer Learning for Speech Recognition
Transfer Learning for Activity Recognition
Federated Learning for Personalized Healthcare
Concluding Remarks