Jenny Benois-Pineau - Multi-faceted Deep Learning: Models and Data
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- Book:Multi-faceted Deep Learning: Models and Data
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This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problemoriented chapters.
The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks.
Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.
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This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
And now, if you will set us to our task,
We will serve you four and twenty hours a day...
Rudyard Kipling, The secret of machines
We dedicate this book to our students.
Be curious, be inventive, persevere and
serve the Dame Science!
Today, artificial intelligence approaches penetrate all areas of societal activity. One of its main branches, artificial neural networks, got a new life with the drastic augmentation of computational capacities due to graphical processing units and cloud computing. Neural networks have become deep. Deep learning is now a winner in all supervised machine learning approaches which have been ever used for data mining and decision-making.
These tools are specifically interesting in the field which has been traditionally called multimedia. Indeed, this field supplies a huge amount of heterogeneous data: images, video, audio and music, text, and multimodal signals. Furthermore, these data have a spatio-temporal grid structure which is convenient for one of the varieties of deep learning networks, such as convolutional neural networks.
Hence, in this book, we tried to provide a snapshot of methods, models, and data which are being developed or used in this research community. This book is a collective work of selected researchers at the French National Network GDR-ISIS and ACM Special Interest Group on Multimedia. We hope this book will be interesting for young researchers, student, and professionals who are employing existing models and designing new ones in the framework of deep learning.
The editors of this book acknowledge the French National Research Network CNRS GDR-ISIS and also ACM-SIGMM which enabled the authors of this book to collaborate together.
Artificial Intelligence (AI), and particularly Deep Learning, is changing our daily life. In last few years, this domain gained much interest both from theoretical and practical point of views. AI based solutions are deployed in many applications and are used in many fields. This includes financial applications, security, trading, autonomous vehicles, etc.
Deep Learning gained very high interest with the huge amount of available data and the new computation capabilities. It benefits also from a long research activities on neural networks started by the first works of W. McCulloch and W. Pitts on 1943 who first defined the formal neuron and crowned by the award of the Turing prize to Y. LeCun, Y. Bengio and G. Hinton in 2018.
In this book, we present most popular Artificial Intelligence methods for data mining of nowadays form multifaceted perspective: in problems, methods and data. The book gives a rich overview of ongoing research in the community and is written by the first rank international researchers.
The book starts by introducing the design and implementation of various architectures for Deep Learning, together with optimization algorithms. It discusses the most state-of-the-art networks, such as Artificial Neural Networks, Convolutional Neural Networks and Recurrent Networks. Then it presents some other models like Generative Neural Networks, Autoencoders and Siamese CNNs.
As a first application of Deep Learning methods, we consider its use for semantic segmentation. A chapter reviews the image semantic segmentation task and recent advanced strategies to face typical training issues (few training samples, specific data, strong target imbalance, ) in a variety of application domains. Another chapter considers image and video captioning using deep learning. It aims at giving insights on how to generate descriptive sentences from images and videos. A third application investigates the use of the 3D Convolutional Neural Networks for action recognition with application to sport gesture recognition.
As mentioned above, the impressive success of Deep Learning is due to the huge amount of available data. However, for supervised learning, this data have to be labeled. In a dedicated chapter, we present solutions based on three families of methods for learning with less expensive labelling. We detail an approach based on these methods applied for using incomplete annotations for medical image segmentation.
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