Dual Learning / by Tao Qin.

Format:

Book

Author/Creator:

Qin, Tao, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Machine learning.

Natural language processing (Computer science).

Artificial intelligence.

Computational linguistics.

Computer vision.

Machine Learning.

Natural Language Processing (NLP).

Artificial Intelligence.

Computational Linguistics.

Computer Vision.

Local Subjects:

Machine Learning.

Natural Language Processing (NLP).

Artificial Intelligence.

Computational Linguistics.

Computer Vision.

Physical Description:

1 online resource (XV, 190 pages) : 52 illustrations, 24 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

Many AI (and machine learning) tasks present in dual forms, e.g., English-to-Chinese translation vs. Chinese-to-English translation, speech recognition vs. speech synthesis,question answering vs. question generation, and image classification vs. image generation. Dual learning is a new learning framework that leverages the primal-dual structure of AI tasks to obtain effective feedback or regularization signals in order to enhance the learning/inference process. Since it was first introduced four years ago, the concept has attracted considerable attention in multiple fields, and been proven effective in numerous applications, such as machine translation, image-to-image translation, speech synthesis and recognition, (visual) question answering and generation, image captioning and generation, and code summarization and generation. Offering a systematic and comprehensive overview of dual learning, this book enables interested researchers (both established and newcomers) and practitioners to gain a better understanding of the state of the art in the field. It also provides suggestions for further reading and tools to help readers advance the area. The book is divided into five parts. The first part gives a brief introduction to machine learning and deep learning. The second part introduces the algorithms based on the dual reconstruction principle using machine translation, image translation, speech processing and other NLP/CV tasks as the demo applications. It covers algorithms, such as dual semi-supervised learning, dual unsupervised learning and multi-agent dual learning. In the context of image translation, it introduces algorithms including CycleGAN, DualGAN, DiscoGAN cdGAN and more recent techniques/applications. The third part presents various work based on the probability principle, including dual supervised learning and dual inference based on the joint-probability principle and dual semi-supervised learning based on the marginal-probability principle. The fourth part reviews various theoretical studies on dual learning and discusses its connections to other learning paradigms. The fifth part provides a summary and suggests future research directions.

Contents:

Chapter 1. Introduction

Chapter 2. Machine Learning Basics

Chapter 3. Deep Learning Basics

Chapter 4. Dual Learning for Machine Translation and Beyond

Chapter 5. Dual Learning for Image Translation and Beyond

Chapter 6. Dual Learning for Speech Processing and Beyond

Chapter 7. Dual Supervised Learning

Chapter 8. Dual Inference. Chapter 9. Marginal Probability based Dual Semi-supervised Learning

Chapter 10. Understanding Dual Reconstruction

Chapter 11. Connections to Other Learning Paradigms

Chapter 12. Summary and Outlook.

Other Format:

Printed edition:

ISBN:

978-981-15-8884-6

9789811588846

Access Restriction:

Restricted for use by site license.