Few-Shot Learning
2024 | AI Dictionary
What is Few-Shot Learning: Ability to learn from very few examples of each class, enabling efficient learning in scenarios with limited data.
What is Few-Shot Learning?
Few-Shot Learning is a machine learning technique where a model learns to perform tasks with very few labeled examples. Unlike traditional models that require large datasets, Few-Shot Learning aims to generalize from minimal data, making it useful in scenarios where labeled data is scarce or expensive to obtain.
Types of Few-Shot Learning
- Meta-learning: Trains a model to learn how to learn, enabling it to quickly adapt to new tasks with few examples.
- Transfer Learning : Leverages knowledge from pre-trained models on similar tasks to improve performance on new tasks with limited data.
- Siamese Networks: Uses pairs of input data to learn a similarity function, often applied in one-shot and few-shot scenarios.
Applications of Few-Shot Learning
- Medical Imaging: Identifying rare diseases from a small number of labeled medical images.
- Robotics: Enabling robots to learn new tasks from a few demonstrations or examples.
- Natural Language Processing: Enhancing models to understand and generate text with limited training data.
Example of Few-Shot Learning
In facial recognition , Few-Shot Learning allows a model to identify a person with only a few images of that individual, overcoming the challenge of requiring large datasets for each person.
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