In traditional machine learning, a model learns by example. Show it thousands of pictures of cats, and it learns to identify cats. But what if we don’t have any labeled examples of a specific category? That’s where ZSL comes in. It enables models to predict classes they have never seen during training, using prior knowledge and contextual understanding.
Scenario: An emerging e-commerce platform introduced a new category of eco-friendly products. With limited data on user interactions with this category, traditional models struggled to recommend these products effectively.
Solution with ZSL: The platform utilized zero-shot learning, drawing parallels with user interactions from other eco-conscious categories. Result? The new product range saw a 35% increase in user engagement within the first month, without extensive retraining of their models.