One-shot learning has been an active field of research for many scientists who are trying to find a cognitive machine that is as close as possible to humans in terms of learning. As there are various theories about how humans do one-shot learning, we have a lot of different deep learning methods that we can use to solve it. This section of the book will focus on metrics-based, model-based, and optimization-based deep learning architectures to tackle one-shot learning problems, along with their implementations.
This section comprises the following chapters:
- Chapter 2, Metrics-Based Methods
- Chapter 3, Model-Based Methods
- Chapter 4, Optimization-Based Methods
