Few-Shot Learning

Few-Shot Learning is a machine learning approach that enables models to quickly adapt to new tasks using only a small number of training examples, typically ranging from one to dozens of samples per class or task. This technique leverages pre-trained knowledge and meta-learning capabilities to generalize from limited data by identifying patterns and relationships that transfer across similar tasks. Few-shot learning proves particularly valuable when labeled data is scarce, expensive to obtain, or when rapid adaptation to new domains is required. The approach encompasses various strategies including prototype-based methods, metric learning, and gradient-based meta-learning algorithms that optimize for quick adaptation rather than extensive training. Advanced implementations utilize techniques like episodic training, support-query set frameworks, and contrastive learning to enhance performance with minimal examples. Few-shot learning enables practical AI deployment in specialized domains, personalized applications, and scenarios where traditional supervised learning approaches would be prohibitively expensive or time-consuming.