Dual Learning 1st Edition by Tao Qin 9789811588846 9811588848

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ISBN 10:9811588848
ISBN 13:9789811588846
Author:Tao Qin

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.

Dual Learning 1st  Table of contents:

1. Introduction

Part I. Preparations

2. Machine Learning Basics

3. Deep Learning Basics

Part II. The Dual Reconstruction Principle

4. Dual Learning for Machine Translation and Beyond

5. Dual Learning for Image Translation and Beyond

6. Dual Learning for Speech Processing and Beyond

Part III. The Probabilistic Principle

7. Dual Supervised Learning

8. Dual Inference

9. Marginal Probability Based Dual Semi-Supervised Learning

Part IV. Advanced Topics

10. Understanding Dual Reconstruction

11. Connections to Other Learning Paradigms

Part V. Summary and Outlook

12. Summary and Outlook

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