Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. In recent years, meta-learning, or learning to learn, has become increasingly fashionable. Meta-Learning creates computational mechanisms to methodically and efficiently adapt to new tasks, rather than creating AI systems from the ground up for each machine learning activity. Deep neural networks face several fundamental limitations, including a high data demand, computationally expensive training, and limited ability for task transfer.
Meta-learning, often known as learning to learn, is the discipline of methodically examining how different machine learning algorithms perform on a variety of learning tasks and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. This not only speeds up and improves the design of machine learning pipelines and neural networks, but also allows us to replace hand-engineered algorithms with fresh data-driven approaches.
The meta-learning paradigm has the potential to address these issues. This book will present overview of the Meta-Learning framework with imaging applications. Many Book Chapters will include meta-learning as a unifying theory, as well as common versions including model-agnostic learning, memory augmentation, prototype networks, and learning to optimize.
The book will bring together many experts from the domains of machine learning and imaging application to explore the current state of Meta-Learning, its application to medical imaging and health informatics, and its future directions. This book will give an overview of the Meta-Learning framework in Imaging Application