Definition
One-shot learning is a specific type of few-shot learning where the algorithm must generalize to new classes or tasks after seeing only one positive example during training. This approach mimics human cognitive abilities, allowing us to recognize objects or concepts after minimal exposure. It relies heavily on feature extraction and similarity metrics rather than extensive statistical pattern recognition over large datasets, making it crucial for scenarios with scarce data.
Summary
A learning paradigm where a model learns to perform a task from a single labeled example.
Key Concepts
- Few-shot learning
- Feature extraction
- Similarity metrics
- Generalization
Use Cases
- Facial recognition with limited samples
- Handwriting identification
- Medical diagnosis from rare cases