In this book, we have learned about various forms of architectures for deep learning, and various techniques and methods, ranging from manual feature extraction to the variational Bayesian framework. One-shot learning is a particularly active field of research as it focuses on building a type of machine consciousness more closely based on human neural abilities. With advancements made in the deep learning community over the past 5 years, we can at least say that we are on the path to developing a machine that can learn multiple tasks at once, just as a human can. In this chapter, we will see what other alternatives there are to one-shot learning, and discuss other approaches that haven't been explored in depth in this book.
The following topics will be covered:
- Recent advancements
- Related fields
- Applications
