Zero-Shot Learning

Overview

A machine learning paradigm where a model is trained to recognize or classify objects or concepts that it has not encountered during training. This is achieved by leveraging prior knowledge about the relationships between different classes or by utilizing auxiliary information, such as semantic descriptions or attributes. Zero-shot learning aims to bridge the gap between seen and unseen classes, enabling models to generalize to novel situations and new types of data, without needing explicitly trained data for these new classes or entities.

Recognizing New Concepts

Zero-shot learning enables a model to recognize or classify new things, for which they haven't had any specific training.

Using Class Relationships

This is achieved by using knowledge of relationships between the classes of objects.

Situations With Limited Data

This method is useful in situations where the training data is impossible, or very difficult to obtain.

Related Concepts

• Few-Shot Learning
• One-Shot Learning
• Transfer Learning
• Meta-Learning