Hands-On One-shot Learning with Python

Types of one-shot learning

There are various approaches to solve one-shot learning. Roughly speaking, they can be organized into five main categories:

• Data augmentation methods
• Model-based methods
• Metrics-based methods
• Optimization-based methods
• Generative modeling-based methods

The following diagram shows the categories of one-shot learning:

Data augmentation is the most commonly used method in the deep learning community to add variations to data, increase data size, and balance data. It's achieved by adding some form of noise in the data. For instance, images might be scaled, translated, and rotated; whereas in a natural language processing ...