Few-shot learning is a technique where a model learns to perform tasks with only a small number of training examples. Unlike traditional machine learning methods that require large datasets, few-shot learning focuses on generalizing from a limited set of samples. The key to this approach is to leverage prior knowledge or learned representations from related tasks. This enables the model to make predictions or understand new classes with minimal data.
One common method in few-shot learning is using a meta-learning framework, where the model is trained on a variety of tasks. During this process, the model learns not just to solve specific problems but also to adapt quickly to new ones based on a few examples. For instance, suppose a model is trained with images of animals: it may learn general features like shapes and colors. When presented with a few images of a new animal, it can recognize patterns and make educated guesses about that animal's category based on the features it has learned from previous tasks.
Another approach is to use embeddings, which map input data into a vector space where similar items are located closer together. When a few examples of a new class are provided, the model can compare these to the existing representations in the embedding space. For example, if a model has seen multiple types of fruits before, receiving just a few images of a new fruit will allow it to identify its similarities with the known fruits. Ultimately, few-shot learning models capitalize on their ability to generalize from related experiences, streamlining the process of learning new tasks with minimal data input.