Few-shot Learning

Few-shot Learning

Few-shot learning is a subfield of machine learning that aims to train models capable of recognizing new classes or making predictions based on a limited number of labeled examples. Traditional machine learning algorithms typically require a large amount of labeled data to perform well. However, in few-shot learning, the focus is on training models with the ability to generalize from a small number of examples.

The aim of few-shot learning is to bridge the gap between traditional supervised learning and human-like learning, which often requires only a few examples to understand and recognize new concepts. The challenge lies in developing techniques that can effectively learn from these limited examples to make accurate predictions on unseen data.

Several approaches have been proposed in few-shot learning, such as meta-learning or learning to learn, where models are trained to adapt quickly to new tasks by leveraging prior knowledge acquired from training on similar tasks. Another approach involves using generative models to synthesize additional training examples to augment the limited labeled data.

Few-shot learning is particularly useful when faced with scenarios where gathering large amounts of labeled data is expensive, time-consuming, or impractical. It finds applications in various domains, including computer vision, natural language processing, and speech recognition.