Springer, 2021. — 321 c. — ISBN 978-3-030-74477-9.
Многогранное глубокое обучение: модели и данные
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 problem–oriented 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.
Deep Neural Networks: Models and Methods
Deep Learning for Semantic Segmentation
Beyond Full Supervision in Deep Learning
Similarity Metric Learning
Zero-Shot Learning with Deep Neural Networks for Object Recognition
Image and Video Captioning Using Deep Architectures
Deep Learning in Video Compression Algorithms
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
Deep Learning for Audio and Music
Explainable AI for Medical Imaging: Knowledge Matters
Improving Video Quality with Generative Adversarial Networks