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Dual Learning

Qin, Tao.

Dual Learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dual Learningby Tao Qin.
Author:	Qin, Tao.
Published:	Singapore :Springer Singapore :2020.
Description:	xv, 190 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-15-8884-6
ISBN:	9789811588846$q(electronic bk.)

Dual Learning
Qin, Tao.

Dual Learning[electronic resource] /by Tao Qin. - Singapore :Springer Singapore :2020. - xv, 190 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9789811588846$q(electronic bk.)

Standard No.: 10.1007/978-981-15-8884-6doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Dual Learning
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245 1 0 $a Dual Learning $h [electronic resource] / $c by Tao Qin.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xv, 190 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 0 $a Natural language processing (Computer science) $3 200539
650 0 $a Artificial intelligence. $3 194058
650 0 $a Computational linguistics. $3 181250
650 0 $a Optical data processing. $3 185609
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Natural Language Processing (NLP) $3 826373
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Computational Linguistics. $3 274669
650 2 4 $a Image Processing and Computer Vision. $3 274051
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773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-15-8884-6
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