Glossary
Zero-Shot Learning
Zero-shot learning is a machine learning approach that enables a model to recognize and classify objects it has never seen before. Unlike traditional supervised learning, where a large amount of labeled training data is required for each object category, zero-shot learning allows the model to generalize its knowledge to new categories using only a small set of labeled examples.
The key idea behind zero-shot learning is to leverage a semantic space that captures the relationships between different object categories. This semantic space, often represented as a continuous vector, encodes the attributes and characteristics shared by objects. By mapping objects onto this space, the model can learn to understand the similarities and differences between categories, even if it hasn't seen specific examples from certain categories during training.
To perform zero-shot learning, the model first needs to be trained on a dataset containing labeled examples from a set of seen categories. These examples serve as the basis for learning the semantic space. Once the model has learned this space, it can then infer the labels for unseen categories by finding the closest vectors in the semantic space.
Zero-shot learning has numerous applications, particularly in scenarios where obtaining large amounts of labeled data is impractical or expensive. For example, in image classification tasks, zero-shot learning can be used to recognize rare or novel objects without the need for extensive labeling efforts. It also allows models to adapt and learn new concepts without retraining from scratch.
In conclusion, zero-shot learning is a powerful technique that enables machine learning models to recognize and classify objects they have never encountered before. By leveraging a semantic space and generalizing knowledge from seen categories, these models can make accurate predictions for unseen categories, making it a valuable tool in various domains.
A wide array of use-cases
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