What is Zero-shot Learning? Explained in Everyday Language for AI Beginners

A. Zhang

4 min read

·

Jun 3, 2024

We have talked about different learnings, from supervised to unsupervised, and reinforcement learning. This time, I will introduce a little bit different one: zero-shot learning.

What is Zero-shot learning (ZSL)?

Zero-shot learning (ZSL) is a machine learning paradigm where a model is able to recognize and categorize instances of new, unseen classes or tasks without having been explicitly trained on those specific categories. This is achieved by leveraging knowledge from other related tasks and using descriptions or attributes to make inferences about the new classes.

Imagine you’ve never seen a zebra before. Someone describes it to you as a horse-like animal with black and white stripes. The next time you visit a zoo, you spot an animal that fits this description. Even though you’ve never seen a zebra before, you recognize it based on the description provided.

This is mainly what zero-shot learning is about :)

The Origin

The concept of zero-shot learning has been around for several years, evolving alongside advancements in AI and machine learning. The term and formal studies on zero-shot learning began to gain more traction in the early 2010s. A notable early…