Meta-learning, also known as “learning to learn,” is a subfield of machine learning that focuses on developing models or algorithms that can learn how to learn from a set of tasks or domains. The goal of meta-learning is to enable models to acquire new skills or adapt quickly to new tasks with limited data.
In meta-learning, the learning process is structured into two levels: the meta-level and the task-level. The meta-level refers to the higher-level learning, which involves learning from a distribution of tasks or domains. The task-level refers to the lower-level learning, which involves learning within a specific task or domain.
Meta-learning algorithms typically learn from a set of tasks or domains and aim to acquire knowledge or prior information that can be generalized to new tasks or domains. This knowledge can then be used to facilitate faster learning or adaptation when presented with new, unseen tasks or domains.
Few-shot learning is a specific application of meta-learning that focuses on training models to recognize or classify new classes or categories with only a few examples per class. In few-shot learning scenarios, traditional machine learning models may struggle to generalize well due to the limited amount of labeled data available for each class.
Meta-learning approaches for few-shot learning aim to leverage prior knowledge or information learned from similar tasks or domains to improve the model’s ability to generalize to new classes or categories with limited training examples. This involves learning generic features or representations that are transferable across tasks, allowing the model to adapt quickly to new classes or categories with only a few labeled examples.
By employing meta-learning techniques, models can effectively learn from a few examples and generalize their knowledge to new, unseen tasks or classes. Meta-learning and few-shot learning are active areas of research with applications in various domains, including computer vision, natural language processing, and robotics.