Definition
Zero-shot learning enables a machine learning model to classify instances of classes that were not present in its training dataset. Instead of relying on labeled examples for every possible class, the model uses auxiliary information, such as textual descriptions or attribute vectors, to infer relationships between known and unknown classes. This approach significantly reduces the need for extensive labeled data and allows models to generalize to new concepts based on learned semantic structures.
Summary
Zero-shot learning is a technique where a model performs tasks on classes it has never seen during training by leveraging semantic associations.
Key Concepts
- Generalization
- Semantic Embeddings
- Attribute Learning
- Transfer Learning
Use Cases
- Classifying rare animal species
- Identifying new product categories
- Adapting to emerging threats without retraining